{"type":"session","version":3,"id":"426dcfc9-f808-4336-99ac-c90fd47461e2","timestamp":"2026-04-11T21:04:23.138Z","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. Use any relevant skills in this directory that match the task. 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":1775941463412}}
{"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. Use any relevant skills in this directory that match the task. 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":1775941463412}}
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{"type":"tool_execution_end","toolCallId":"call_E7N3iMsWycEu1FerOd38WIeN|ZtkPQqOMSRtYVG7bXU8jIDJEHrJ6N0YjpWiavt8HaqhdTA4lnumrbC7qUw/sjaWULGLo77Dme2ddD1ZwxK3PK7Re0tYmvXpit5QcaCua9UcxQLDNysT1RfW3BeyOvjECw3+12wtq24hyI7fcDNsXxTPuL72/zeUML6qAX05dmiFEbyP2LS38CqTTwtdLc8I4tt28iW5IZgqTQD0rfmWp7FEDnJ1ngbY318CfY7e4ZbxVS2L6UICQYxxPxEfCZiiXhdzMhTiN8GdVa/99KJthLnOdgdaZakJis9lbR/VbCcYsRIAX7LvDvb2PtYbeUTVbLyDs/n5MNN/wkHYYpBm9HfPm+hWtMNf2EwoZGEEvenkqNF+4J7K2JQySwuWscDmzyHCix9TgkVF49psNyjTu+0Y5vOWHMuM=","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_E7N3iMsWycEu1FerOd38WIeN|ZtkPQqOMSRtYVG7bXU8jIDJEHrJ6N0YjpWiavt8HaqhdTA4lnumrbC7qUw/sjaWULGLo77Dme2ddD1ZwxK3PK7Re0tYmvXpit5QcaCua9UcxQLDNysT1RfW3BeyOvjECw3+12wtq24hyI7fcDNsXxTPuL72/zeUML6qAX05dmiFEbyP2LS38CqTTwtdLc8I4tt28iW5IZgqTQD0rfmWp7FEDnJ1ngbY318CfY7e4ZbxVS2L6UICQYxxPxEfCZiiXhdzMhTiN8GdVa/99KJthLnOdgdaZakJis9lbR/VbCcYsRIAX7LvDvb2PtYbeUTVbLyDs/n5MNN/wkHYYpBm9HfPm+hWtMNf2EwoZGEEvenkqNF+4J7K2JQySwuWscDmzyHCix9TgkVF49psNyjTu+0Y5vOWHMuM=","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":1775941484805}}
{"type":"message_end","message":{"role":"toolResult","toolCallId":"call_E7N3iMsWycEu1FerOd38WIeN|ZtkPQqOMSRtYVG7bXU8jIDJEHrJ6N0YjpWiavt8HaqhdTA4lnumrbC7qUw/sjaWULGLo77Dme2ddD1ZwxK3PK7Re0tYmvXpit5QcaCua9UcxQLDNysT1RfW3BeyOvjECw3+12wtq24hyI7fcDNsXxTPuL72/zeUML6qAX05dmiFEbyP2LS38CqTTwtdLc8I4tt28iW5IZgqTQD0rfmWp7FEDnJ1ngbY318CfY7e4ZbxVS2L6UICQYxxPxEfCZiiXhdzMhTiN8GdVa/99KJthLnOdgdaZakJis9lbR/VbCcYsRIAX7LvDvb2PtYbeUTVbLyDs/n5MNN/wkHYYpBm9HfPm+hWtMNf2EwoZGEEvenkqNF+4J7K2JQySwuWscDmzyHCix9TgkVF49psNyjTu+0Y5vOWHMuM=","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":1775941484805}}
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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":1775941484805}]}
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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":1775941486903}]}
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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_4UxcGQAvh25lvs95X9QObEX5|bM8Oo3ksdF2FRkVBtN9NxX5v0hpdxQKyYOlH0ItFOb82TvKFDjnMmY0C56quOCl0dEpKwLiskKo8VgscT2wmkSG8K0GxhC+ENUg5yrGw42nMZPPEMbhhXW+cQEjyB1pgJgzSltZebiG8xLcxHc7ppfeUc19F6DPEpRjDIidX4ZMP/hHr5vj+OcG37rFjzk1ip7rQVFlsGmz24cs1iLNRqQuPxBeGICp97VOJJVL+FBKF4A7eNoytPjps0gxHLZVSItMDN0HkIOnBQShl9l+bIn77oDWaUVxXEF4d2Yuqzuf/FOGYtplTl0d+A425u4E32Y/I76/ocA4DJ9Bq9bHbk7PWboaep+2r6RzyWjNJavw+u/Xwgpk1DZbvVeehiiaWMjZeoO5fqUVRxeIVuuJd4XoHsdvqxiw=","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":1775941488445}}
{"type":"message_end","message":{"role":"toolResult","toolCallId":"call_4UxcGQAvh25lvs95X9QObEX5|bM8Oo3ksdF2FRkVBtN9NxX5v0hpdxQKyYOlH0ItFOb82TvKFDjnMmY0C56quOCl0dEpKwLiskKo8VgscT2wmkSG8K0GxhC+ENUg5yrGw42nMZPPEMbhhXW+cQEjyB1pgJgzSltZebiG8xLcxHc7ppfeUc19F6DPEpRjDIidX4ZMP/hHr5vj+OcG37rFjzk1ip7rQVFlsGmz24cs1iLNRqQuPxBeGICp97VOJJVL+FBKF4A7eNoytPjps0gxHLZVSItMDN0HkIOnBQShl9l+bIn77oDWaUVxXEF4d2Yuqzuf/FOGYtplTl0d+A425u4E32Y/I76/ocA4DJ9Bq9bHbk7PWboaep+2r6RzyWjNJavw+u/Xwgpk1DZbvVeehiiaWMjZeoO5fqUVRxeIVuuJd4XoHsdvqxiw=","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":1775941488445}}
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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":1775941488445}]}
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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}
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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":1775941491338}}
{"type":"message_end","message":{"role":"toolResult","toolCallId":"call_7gQMmqmcbg0oWk2txzMgz116|N22ACjlaPRGxt02gQpgNmQJSeDkWxHzzkQOGuIdh2yX52PIyubIV2/HQAW595wXsryTKS2S2371/8BA9KCCVP/x/fufqF0tod+IVTy46RwODJKm4OOJN8Nbr71sLPYnaGePf3Y5VlZhvgwj5yBoW5M4z4ItvOkhHc/8VrVnI8edg1ICAt2IRiVQ8Yiq9g+pSi/WF1JMCb84MNEdrvrUsUZ0Nn7ksxVMJLAz1CfIjdvSTXaFKtq9wYqMm8jwgG1eG18kEIiA6qofmWYbfPrqVItzq7UzAoUukCTvbV9v65nLYl6wwBFR1F7NFB7s+uZpNLYErgar5iUBY3N5lA0T08qQ3njqBmZh8O1Nfe86qgRuTr4CwkpXzzm2cIck5RjpT4ZlKdVr0TmyaLxVXx4s5vVxNCGo6bHg=","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":1775941491338}}
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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":1775941491338}]}
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{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" instructions","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
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{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" select","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
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{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" definitely","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" include","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" cav","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"eman","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**Selecting","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**\n\nThe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" user","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" wants","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" me","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" select","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" so","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’ll","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" format","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" my","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" output","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" with","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" exactly","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" three","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" lines","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ...,","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Order","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ...,","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Why","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ...","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \n\nI","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" should","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" include","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" cav","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"eman","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skill","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" since","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" must","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" always","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" load","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" it","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" according","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" previous","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" instructions","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" For","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" list","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I'll","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" likely","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" include","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" options","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" such","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" as","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" possibly","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" while","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" clearly","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" noting","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" that","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" cav","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"eman","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" is","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" crucial","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" even","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" if","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" it's","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" not","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" part","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" of","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" specific","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" directory","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" mentioned","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" by","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" user","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**Choosing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**\n\nThe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" user","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" wants","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" me","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" select","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" relevant","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" their","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" task","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" likely","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" focusing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" just","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" on","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" directory","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" While","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" must","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" always","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" load","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" cav","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"eman","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skill","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" might","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" not","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" mention","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" it","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" explicitly","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" in","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" this","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" response","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\nI'll","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" format","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" my","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" output","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" in","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" three","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" lines","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"Skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ...","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"Order","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ...","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"Why","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ...","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" For","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I'll","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" pick","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" as","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" primary","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" maybe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" add","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" since","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" it's","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" mandatory","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" for","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalization","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" This","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" keeps","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" my","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" response","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" concise","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" aligned","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" with","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" their","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" request","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"!","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**Selecting","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" with","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" steps","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**\n\nI'm","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" thinking","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" about","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" steps","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" needed","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" for","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" this","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" task","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" The","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" teach","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-im","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"pe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"cc","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"able","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skill","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" may","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" be","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" necessary","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" if","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there'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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there'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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" there's","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there'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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there'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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" no","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" context","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" but","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" check","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" for","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" .","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"im","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"pe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"cc","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"able","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".md","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" file","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" first","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \n\nThe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" user","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" requested","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skill","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" selection","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" not","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" execution","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" but","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" choosing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" right","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" is","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" crucial","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Since","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalization","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" requires","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" have","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" include","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" that","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skill","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" as","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" well","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" The","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" likely","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" order","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" of","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" would","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" be","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ->","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ->","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" This","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" keeps","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" everything","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" organized","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" aligned","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" with","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" instructions","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"!","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**Choosing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" order","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**\n\nI","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" come","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" up","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" with","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" three","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" concise","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" lines","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" for","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" being","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" selected","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" The","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" focus","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" will","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" be","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" on","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" only","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" specifically","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \""}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \n\nThe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" order","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" is","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" likely","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ->","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ->","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" because","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" gathers","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" context","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" can","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" trigger","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" teach","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-im","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"pe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"cc","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"able","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" if","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" needed","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" then","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" identifies","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" reusable","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" components","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" real","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize real"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize real"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"igns","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" UI","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" tokens","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I'll","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" keep","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" it","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" clear","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" accurate","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" with","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" instruction","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \n\nHere","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’s","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" how","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" that","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" looks","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \n\nSkills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"  \nOrder","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ->","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ->","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"  \nWhy","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" gathers","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" context","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" may","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" trigger","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" teach","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-im","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"pe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"cc","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"able","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":";","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" identifies","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" consolid","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"ates","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" components","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":";","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" real","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize real"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize real"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"igns","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" usages","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" system","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**Draft","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Draft"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Draft"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"ing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" final","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" output","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"**\n\nI'm","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" preparing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" create","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" my","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" final","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" response","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" with","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" three","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" precise","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" lines","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" First","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" need","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" list","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \n\nThe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" order","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" will","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" be","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ->","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ->","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" since","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" that","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" reflects","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" necessary","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" steps","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" For","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" reasoning","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" I'll","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" say","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"Gather","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" context","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" (","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"run","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" /","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"teach","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-im","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"pe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"cc","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"able","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" if","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" needed","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"),","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed),"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed),"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" reusable","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" card","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" components","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" canonical","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" tokens","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" then","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" code","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" replace","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" hard","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-coded","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" values","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" migrate","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" uses","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" back","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" system","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".\"","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" \n\nThis","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" keeps","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" everything","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" clear","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" stays","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" within","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" three","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-line","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" format","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format."}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Here","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"’s","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" my","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" final","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" version","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\nSkills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"  \nOrder","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ->","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" ->","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"  \nWhy","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" Gather","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" context","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" (","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"run","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" /","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"teach","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-im","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"pe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"cc","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"able","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" if","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" needed","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"),","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed),"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed),"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" reusable","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" card","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" components","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" canonical","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" tokens","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" then","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" code","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" replace","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" hard","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"-coded","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" values","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" migrate","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" uses","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" back","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":" system","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_delta","contentIndex":0,"delta":"\n\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"thinking_end","contentIndex":0,"content":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design 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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_start","contentIndex":1,"partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"Skills","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"Order","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" ->","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"LElKq9fY8CyU9HssDOv33EsHiFJrUknrBQonL6f33JXXDPb1TUnwO+uhz+quQp+kdfc4tm4gQRMxLoG7k8tHFAtj/17/bnj45NO8JAGgtUneVlpSpCQopg4Awtn8FC5hP52gqkFuIHosXKicHP3eyGx8bX7IdRj37bQouPqOtPpORVozRhJD469V4b1/cTFpss5gEMB/zRShsBrcMS/ooPtEkZnk8eMxrmgpQD/SzX2LkULVrMkr9EKvjwLhaxxYHngnkA5d40MPu01dsafCGymOOwTyDuVo40N3rs9LmowVAR08RR+OjnaqKv+yoQtAi6Dj2vyXMDWF0OBXsqDo2MDAsb4OEETRN+IaKFNPoRc55WxwxL1SL0Wucvd2dsSAB70pZmPp7qMy9VP8oLCR1S21RwrJxeHQdgw/7bDfJNijr1NVzkDrBhI1K5RpZl++Z1/fTP3GqH01ESV/XN88rXjmtZ6XJGhteRr/t5B66Qe4BC7oQKl+LvIeUUKFUX0nIJJkMmjuUXl2/uBvz0mdlipbCLxfNmbAkxge3sSsLZ9C8vtPx/lC1GL08TGu/BhEB1ru5VeuuTVlH+8fWfuhukr8iAE6wKlPjuyBMyp7vS6+wvU+Dadz/OBJIraI1G6mNV6ze12SIDgRQu5XAWdhdhLnkDc3lVFkCn09bMGyGwzkSRFpfcCj14iqZ+WW/RgA4FymCxQyVnKL7jl81LvKIVC9ChMBU8pt/jzD5gqe2CsE4wB9+dZUscXg1yAvZo/e27TdNYDSyUwiG589ja8plRtqGxImAIQruQh6pZ558RKSqAbw1KYsJqLiP7kxpu/FlTbqTzSRdpXiPeWSSJGTfrW3HqbDaRE2KY6T4Y43lEKB3tG+cQKgHx7Cl1V1fH7YieRcQoBbGQvuhATha9S0NQnztmcgaCKM88v9Sys7Z1KmteatGeHNZ/qwhMfUjWQPCAQedTskRIwd7UpqcqMq8L1vL2DYquRZ+z5urQiC6YISJtSBVqH7thMlQPnp6rao0Ffw6Si/qFtfZMo7lrDWHtKpUz+M/4HztXUr4hieN1gI7XtsTikDpQQVOYG5Xj+s0BoHElJk9WXZlilBB5JvQpoTjS1zyA2klMtQukSxnCIiLfN5PxkKl2LLYItfFnpfQ21c6x68adaXsxQw3/2UUNLghUEFFtg+LRpdI8nG5JF3d+lhFTpx0VVaqeGUhldGRvkq14KATbpn1opnnJ6a64IYOJP0Or23ao9qDY5jnnXexJUtimOeTWz0xT1y3Q4d0IiV8TLC6lkWnueRgnoO3n45JIQQHQpzn7pe9w9rzoMQvo3rlo8veNsbDTRmFiNm0GMOWwdGlR08qzxYl0EvNfavTGtXxYDqPf4N7GITN2jj+6BCk144gH+z1HlklevNkQqhsguHreqe19x3j7X6KlK4GdP5WQujHTiixnVZtB7mx+b4Hdka0C8fHDl6Py8zzkQTvYvs064yF7H7fCPlXrfuFMwczM8VGVa4ayElG37/tmQwnl0jICE6ZJLXJS1Rkbfg3SkPl1Re3vZiVSRCpjFcETjkBzsQzeUyLSHTHy8YvH4pkxyM84jVFco0heyJpOzlP0gI039n4NZQSWvG4xpsDT+VORtZN4hbZZQ1IrXiaeOKg0quEBw9gcYmywDDhfpnrIr8Iwne+FueTXD6vXCLCQ6FwufmpUa8V4jMARnobO6gC+/ABxGY/TcSVspozjBg/98mcZRsSkfMNIvKdqtIxeo+YnSNbsSHIGKBmngRusPrs6w4aackcY+gtapGI8vW7a6DSeigr3lB9sPkrk6CnonnT672s19T7veHQETWfb5O9Lp9CP2EG7qFjDIrd6N04k2OLNTCD5k4dwNxSG17IjU8FxSKT4Ffc/Q8jn4P+GFkj7+81+dWZuy0Cl/qJz3pliVxOuAxMU/4zmUL7zdaAFGhQnw5hqfKyVq5rjY12bUbf0oN9ZFv712j4KkxgMTKQ7hDw7ell9xCGCuA5KVpcVSfT4Rwzr5q1mgy0Jgxw4E//KMKjn9qqe/5sXbzIQkdbzy0wEXZBqbHKwtjAx+sxzWDtANGZdAu2QmE8BuhDQSBqot/R3QRzQzx5RuixrePld1lWflWgkaBwLvKcmP9UdnbXPckL2a2+/LFecgksCvVooN8zyiH1W5YXiRWVKH9TE5x2cI2X7SeMRshGUzZV9yfLR/RKMLTe329gsrWLsCcGjxzFpywHhlt7FAIWoiiRCtMU4arzj4kJjKz26/KKCGCEx1OgYD4GqY7IDKOriMOZuSw1XfXrhxKtU4ddfqXI+3gMxSS4Y03mc3yvonwJxzd1qXk0LEOwF6gj8xoXkpcyqoxNr8mSQNPhJeWhIFg1mlyyIRUVa05SIFKld1qc0AMk1n8/tCvzHpCbFScXL6o3o5b1EJetyGxdRmppr+AoovtQ03y0G2Z1uUR23AtbxkiZWlCB+g4Vx5eIm8e8uLw2s/AG6Qa8cPSKnhgJ2b9i3UKpVS2mHezLGTKHNsEVGxjKoAdLDrEaj+S6y10J3RdcQ5r70YuMAQKoT/NqYjiE825I2IvS7A7W6BIMFMuf2sPhCpT2RPtirK/ppC+Z8yGrW9cZFfGeHVTIetLNP4sMCc4R0ZBGcyBEv8gXFxRyQ1WTraIWQMJeMDGSfPwy8kwcammlgVyL53QGU8Yx90hXIiaUX+zbAmHjzk6T+yL/PrD3ed61A/eeyOwcWq0oQk30pvtk4VBeNw9vopFDJPJdoQayQREDTc6u4mGbj8luQlNtwtxaFzKTKjwm8ycMUxzGPyj38UMeuZePcJ3EAeMIAULi/3p0LHKnmJQliIeCMYZ1INd06BRUaH/TxlIICQveurvXIehKny9mq4DDMKStEkXVkMkCg25hlPVkEvHUB24FVjp5emIc2XCpJfN0jUzM59ZK0EepptE/CC5efI6I50BMs/ssxB/5mwDprSwVSHnHlcFZNgCp/ecTyouhS3BxwxfKHFNSEBkTayxz2i7U+GLA9jQ4C6a4oqf8Vq2KM7i7ShwtuBSZvgPWVWl6Vu5epxEatszjkdibEt4HtpcGaZuTdCYR6hUAWYn8IA2BqWS7gojfaJRV/jp0NDYXa9XvEvE9zLPHR3HBQLxEckUYyaoJk4uYeaUbs/ieR594MeMI7BJjaOieu3fXZeZozi+3lREEcDMCIvy7mBUaCBHZNcQFuqhRkRSA9X4CTy/QH0E9KfXkrAchjsQHB8X21Bql2GiWDZ+cthlVnRGmLLQG9/WwwYCrE3/q2T1aBnA6a0GUmH5OcDXdM4hI1NwkjW0hbY3NcleOh9Ad3LQ30Jw+pkEo+cPggLLwjKqUUGwpVM9N7OV8UqobRPP4yR2pi+57NKPBV+0iddQfClw1Uv6LubKBX02uRm/bGCtLqbrGTIlHDJ1aVIa9QoAon0fKfhh7nGAXxO6zIevC0c2UhttpiQEXO22i7orrmEaXYXTZSPKA8Z2BFlUlaHb2WDe/ox4IjQpQ07H+kaYT/Y72j0nrIJ1MYoBQfPToHd1hQG6wMCLZb0l+HwH/FxvlAYwpSj3u0V9D5wmLfVyadoLP4j7b+Xgd/VbR9DXrwsUskOgZEWOOs8CdayrparTSkQFVgYDDaMqgQkNqj1e/0kiXp+RrcFsrNz2r+icUeZ4DHbfrvTB8ENeQwjfpY7e8GmglGtllRAuSPxzuEwx6j6+Gn9hFehSfUNmO0ZPU7on1N1SRE0AqUZSjijRkVxXUkRAxmhg7GIjeP7faySGAGzViWCethRbzThYKDx5kXKjtbebq2wMbaAIamY/5XQsJmXndySfoATjUzR+Hz1ehCFvqWLuPQzr0pyjTindDIWDHwEiG5jn0zJ0NLcYZ9Bc2px+K1+VG+8vpFJPQkFnyLHCpMwNr4ZiOorfYQx6gbLy/AOSCcVEQJkWWLIh4TEWeuLDxYSaZydnwEfLjpjXgbrPi96ZlZcbyULIhTAqg6SFiXQr7W63dnJKzxrwA6QoV3TYQtGwRL2/03yk81+I8waZBFXNLjMABDY/KMc0VznMOU2HWIhHUpkUWcmitO+z2tdSp0K5CBtqFUeQzxHUllSuEj3J4ZLRresIbhf4gXB1eyIKPb0TbPbthVUDG3UVQQrChJ/Jz+E9mGRDQLtN3/I5IA7gqsqII1dzer78KAktdAKEt9S800p6puqQazddq0/Q8Y6ycVjJAEYshQunAjM0ev/TG4j4jjrZjjegNKfUvwKcHzYyNSLNSbqcNRzirL8Q1cGDANFsj4wl+lmggCD7p9G++uwtsWZ+iSkjzr8Q++A4zFwNB+WzxLlfSuQ12eXV0tEXTm1MW4rZ3IQO13DxP1s5m/TgjC7g5NlsdUrprBqvxSeK9ThHjRg+TKp2bYAiqaxd31bvbzu/r00QW9U9Tner9nXn7trZDnbfGRWRypXPCGTTH4u39vgO79x7mAglMMRV5txmFcgcgGAtPHF3Fri7C9vZfzNFAUBc8AWrAoVKrXlJ1bSelEu+j91ixD5OqZgP6upPQLTgASGArpU4AK40TUDQnSl4uy4nbTLN8RQKa5RJYvIvGXOiFao/1QYczOZWAS21BiEBcvmqfN7/MsjcUwjwMTgt6XfSGbGGukP+tusVeHuKA2XvbBULQfYZpIiL8fxrOKvYBTfhtRQB8IJdrYHj/+eMn0JbewYMQ+xrk2eOYqxTMtImotpuA8afG7Xu/0XkyCVBU1ZjFkpARSRYVtl1Se8RWMRzb7nyU0nmOV5GS0VdupQ8Qz02RijQWDtp/rAwlHpFBT6PfnnGM14N9oFpTc7wLrLXa4W5qlfpGeuinvmP54D7wqSr9G0ITNqFoilyPnQvDGCv2wnD8JN1koyOAT2O6SFrkdyTrD6NBVG82kOdPCBpDk5IYx0J5Xq0L8dIw596TwK1khVAcdBG4z0bISDg/ClFBhVrwHid3QX4kKJZA13V5B5tishx+6syMnkmowtM6w9jQRSxjaGFJu5fDC2SHxMHzsAI0bArNl01J8c3jZgCSfn/PoZy8fmBtMuYwsvRxRUFNmVKGfGnvcdApqVE9Fo8d9iHK/tM3UOSlmKQpeb5j7Komnx2O/By2RUzKvSCoJ2eKoRIDlcSQPJvRM9I8Hz74st96mmGwmhIqqkrrZ4Qfe+svK5mFcsQhRQg8vX3vNXifRa8Af/uuGksDfv+QtrLjGnZscmMp9H3lJmO++jyUH6Vy256tDjrSUjK2RBatgt1FJaPdYpQL/OfgzpMD4D980WvC4R3HIDpdtd2mRP2ZttmjBhAKwsbnAnX/+Axvg3eVnUdB8SDPmKLPibT2t44SW2WNZkIwhTH6CD0Wj2xzntFsBp9GJW7gAWWAymGClQQsKkbWFzFiTly6h4QW0Zf4se4yQQJymdfyAR0bzgkH9SM2OttGiNveotV+KcvanThl36/sNBm0MFSp0MDQvO/Gc5YMaZwvW6hwe32v8+c0WAMXrj/wnN2g8CrPPJ6P/GxN8rCIIov5TuTMD4yLeUb8t7BhEBPmL0YNWG41VQMGgsJTwzN50QNz4zkavfikQ37ngPt1OUH+TRVdXKrvwMQvD7O8u91uvCnU8YQzKQkQwqi+JFYJEFow5oVZlrPcJ76d16M5StLuKjCkZ8/EvN5+FcKgLuRK1m6klX1qKxLG1EjL3ORFbGzsECoHqwfzVsV+lWwQTVrvWmEZ5/CPiR3wPvPT4iNIiHwAoPCBip+AWBOmBYbyeMiMvC4GcJwgLAcU7cqag/JlnvnO45qXKtgptzYfilGUBLIdLzaYlsmwbXiC/8P7wof5rwwJnFaqnbcP/HpV7Q9sHMR8dGzw8r+n0hzK3v3wCvF5NO9eP5T8sdAz9ABO7TR1wTxBVvMqr6a64ylrs6KMwENKkMLQhpjJsh8F+qqX14ORJdUPE9R6w7NZKpp4VJnY3j+S/aI0wHl7c1dT20mrrKMl/QoqirKSponRv/aBnLPmm5qb8pRdC62wkDLsyz1EqJzML/Dloaqa1balI5Fkr1utkcqIyewK0xB6OSKlNvfq32bp9HjQSn362o0V1GZL8B8RMxy41bDpbNcARWGc6SrX/ODgEuy27wGnmTlLroJvqZaRm91CFvqY6/XcuJ/nCafDvE0dpNEoegEJXwJOj0e6DcqLyiH7nY21qElokBOTaQ9WvAonGKMry6mASll3//HegHO393Kpvhsh2oKXXOQ0omF3lm67pzB/P3qI6xsNQp4a0MBTyfda6yhHjxTzWYawqIJGK4xUCYAIS7sXrDChUmu7DDFWadWg1s9n8Y4fVzwGCpbXSpJ0j+BRrEVg3t+ZF/IZeJK31zBRUIOfaSTV+GjPBQyCRGsppF0KO9u0456X37azvIuZZvhV3l4dTvAFXyzjTkodJ1OyNCbCtqMx/zBDbOXgihXPqwGp8i+NI8vf3w1L6Klkj1Vg2UfwLO3tI2rbfHJLzv0/VZOcmkEsWKwQm262yFcCaLwtNFdZ/HJEiiHO+Sgy0KIh0PY322Ne3T0uuH653Z2RsvQSO9L8c9GMxkqpRc/FDKHoWLGiXCMOfteTuZjdKtB27Hu699ZjRfbBj5J/2VvkncJGSw7Upwa3pMxxN6UtW+LuR/DR/2079faUazpx0JFy0jsoZj54kKPiJHIuqXIK9u0wkqL1JvCaWkbINtZCOtreFxxf5t6yKEn7BftqTNpYXud64lf62b8nOwLjAOknqtgY8HQsmZQa0lZh1FVL0vHJgJxCmB48vbgxXZreEA1hrf2VNPAPTrQ82v+UDXTEKTC6uR4f7mQulhGknKH2r2q/h7jOUQ2QJgnqOfPmu69q9lsMpjcYQPQGVPcQAqThZt6K7tYKucLqQQKBMNwBgOHDDwPqmCKlOodEP0WYHZsjm1bdFEAWaMlRbQnI5iAZynXHmaGWX1I6E+f4hzkSIEQlLELp23qKl7cDA7Z507MSteCir9Q6IH8B/u4zG9MvRtAZS3qfiEhWKpLAl3fllEvsmDfDkHfkiL9fRQCCfMmKEzJCG82kDbr1BJ8BMJgga3yxMGNwz9u2HSyyV3elyUasblSMdQofiGWZJO8tI3RvfYjBMMTvzoiUBR1KgjjYD+arhjIw46NyYg9S6A2X+Bmg51AiHjhZFl21Fcz7LZLMp+1YToZoWT8CcfdmN15uIqg9zU3g2NWrnXViM92smDtsCiNKle8teB8pKXLn92tTkDza6I/+C27/GL9EtWnDykXt8XtHhGQO6y5fVWDo51hXM9UIuS+wx7NS6plD3YRvrRRq73wyBdtvdNWZzHPO9q1yW6HEsc+wBOLldwFJq9r0vOAoctztp8UiXB/cm30VEQk2iIjsngXJLbbDtKLM5dwwa6kWnY2NmdB0pBUlt8MyrkFiSUGnIelRFGxUTJkZ33AD+cmQyBxQxKg28RtFpssucCPe8xwB/iS6TtwFJZPl8gb0zpDJxbqkUETN5HaP0zRgJ9EkR70fhO61WcSKpqInCOFMRvq2g6Pmvimz9DoRvP9cMJT9gnKIAAmdOPiQLnMb03kc1ibjwHTcE8DkuuIWOUM8r2S2WLHfS9wbOMEs0Sr3/0X9jhEOV3yTK0wOMjUQdp5WF9hoLNqIaIfORA4zeOegFcv4BM007QZ1mmK89N63954c4JqfII5MXrgA+nd/uVx8/ySY4f891PlspSy7yr4A/uyfrD3zzzCjD8OHJEuICUGEu5QBuJQtQ+l+/LFGJieyIR6ED+DZkT23fT8aI/unkntg+u5dP8WV9rEl4dOtuVTZR12ub33RsBm/219PO5NC4kfamFT7w5f8+ibGiqgi7p0alZD453sWM2ErkFB5Ze4OS24RghTQ3NsEeA3Vse6V2sipfdEhH1+PrnE7lM6AkRsutBH60EQ+EUdii8+GmpCLFmg8eJYxH0ugDz8oNcgMqsrwhWEYMx0iUtuCWdJfclK5Sh9RXWIic6rSY2kwFzFWOxvltONdz7Pd7wh5vNx93sO1dj/SISsqk0+yYDvIf++/N9PxLZYeghS1+Yyxh+8ePG1Xw6XBnV2T3uqMGfVHJQzzTPZYGZy0H412A2RtvBLV7K8LHNB8XA7Pz4l1Dgk2dCBFlNgWdkx1wxnpaJ7oKgqdqDx6tBCEwmN35mH8lP3GoQWbibNToFLhX94GR0F+u3mpPs8yZejLckfbttrpka4a20xejYHkDvH2tsTrTPixhO4lYokvVf3C6njbq13nC3VKMa3eDdLZr3JK5VGkNkvvjdzBokrLXsoATQDfYNNvbEuKsdkrn8Z67/fyRqy++5TLhd0R0k7Kcs9UCgVCeSJ7HANBgAbe+iXZ/iJPFsXjzR0BkJOlh044qC9FLtmdJmGBoFgM4vvdQkw7fCSZzYwGCV6JjfsSuMvYjsOEzZ6D8aiHs8dNoY4UCt3f0lBrnFyu2fKAeVsN0j089D84xseWvrehusj9D6EpbEXOGHBY4fkaEtyp3GwEByV09AsKjTwwqwCPE2X37t6aTIq8xIaWG47Uq2K7ClIQISfMiQGx8GzAVINzcZTWkGYEfMlVPqUNBXlxCWnZlOMl7JwVZ25woJc+AKXQnbVhY8+HopdZKSP2DUDctVwlYLbSIDpQU6RZ2VeTO0vigkJWcHJt7eaD++3CHi3tyDfdMGBMxSa6nlNe/obJbT/wl/HzASsHVVxRVRNhh5NwkKJ1EefnhA2en6UPZJn1i6SYEOz647j8P/VhJbk+Z8ftQi0/1hGXnhXxZc8yfnVCysawL3l/Hm7sycFGalT19cxBvWHhuIDW+rjjyzxWWPn6P2HCvCDTyZuAUyTMaAYDOE6WbKqSpEPSg8ppgj2OtLM0N/hF4VtfMB6bCbU6R6o+2IVVItRaR6drZ8PbSiQmby1bvgpRdbiv9r8aWu3D2n41nKpj+5gaFklnAN+NOIgFIRDWTrjLEWK1+Ys9GDs9kKdPXEUJ3ME0+CAsU9YjYTizE95cNVQDncPsJh/xhg2mSLcyM1fDBlQhp8kmF0qiudVFAg8HHFEFOEDuC4i+AHTBG1297LEXZQmX2Gur2kjy7m1Rmgp0gKszs8//lTiB/A8SvtFFdphiOWEaTRHndhbGtu6GqtoTMp7vUN6KrdiUDVrNm19PGSiTu+s/E1ankxpU54zclwx5Fuo2OLxH9JDRz3qcUEWz8hdR+Rz5WHz4y+whS6FmHBLScUgFum0KJ4lpDBfsx/DGfoTqMQQDM/53OCu9fQHqUUi7zw6nTGWBJv4lR6/pVSHltS5vNEH82m78y1A/Zdj3W2HG1Afm3Jwuy3b8wXxMWTRhh6YEZSH0njXmjIjK34UOr72Zj7BEdBpqeV0IU2fIR7TOVYR0KeIGivUQFB9d/KYBcnInzp0ZYg6UwR+lY6Ji8Fxs0eE7yVWChtebpEv3CiTKn6PyN86kxI1lNYP0F97fTu7B9g4JmDDrKXJXdAGMEFa9IeTxznSiQO2KzYmppAGlS65hVMZnqb7Q8Lrio+sGmjgEiczvg5+uWtuC8jupxZ9CVwr7USm4tLx7DoKbt+563JNkqOcb/rzeKAniqjvfFY9doB4IGqI1Id3m8ycqbTXdHXDp1DazxqFMp4LLZys23JcrEng8nDn4Hhg0TShRTzN36OIhPdXchBeQD7asHBkNySehuXdT9WWjS3XozpSGnbm70FHpReynV3y2m+E8Tx3CgP3dELP1IpIjgpU/N4J65ZozKxoJBeM0hRMy5nchuzbLw6BzEtHUlaEOPY+rNweINc+V5g6+LFTErZWXXn3rR35pr7ar1cYL86xp+IDzuWEKyA9y57tghSU44LBewpclIE2nROU6l3OznIJKKC4/K9lVEvam8qsO6jjpUkFa/qjThzyHxDR7Wz9a7y/MHLMf33EMx4n/b/Ny42v0GRFUvG0rIUQLZ+nWwMh+zqBmoIBA3tSRJEMR7mJ/k/oLOo20CvJwRaMzghAjk7nyGlZOC5Hy8Dy04jWcA/C73bFQBVwR1lPgvYYXMpd/SWA/YxJzgjgcWT2nqZJg8r1x+TMB2JjuobvImVjUCzaMmGMyRzMKRvq856XzydEtAexZzOYse6r4weVfHuG87dvqidoF3QFt/P6KpaxQ/EkZnA8FDwjg8YrMy4axlN3TTUI5JIrDqij6R4cpIGzPSik/G9QvtUleC6l0r8xexhTO4QKCX1NMnnQVuOlOlf8nK2aDXTci8c56Q+NgNJO2cYK52WU1xW401iNmXOZX9ZNPtkrUIwXXyOD/Ms8TF4Oy5IyHI1e3jZ430RQWHcZAkroRzAGngeVtTfdhaiPyyvatNGrnGcvt51ctSqLxtzR1P3YSM5hXDeZCwvLfsbYBz0ooOx2EexVkn9P2YOXMQV5ie82mqEPjj39++EYhfnulH8yOQ9+WT8HzvhPG66Lk2znAZkPAKWG7i49kIyIAO0D0vCR0BQvegXwWw7gDt2yKnb9YMd8/HU78rKPkX37U7jWmL/kP1NoYx761GO1iSBYgN6P2yWOh8rIZQyYCgyuHZG7X1mpVuNEVfJLvhyOcO3/nmOP2ckKR7AbobA4FGdmcFkXHJOLFRTvagIESdYdtybJxPqdbshpqCicF2Fz/6jWTNP/G/pXoc4iNkzIOb82iznM/ZrF30Rhzed6qiYZozeIsiRS9/VxYF7iobX9L/N5Xa7p3YeWfTN9djUcGFCLNpqFGoctc8txuM+mGRDedy57kDiQRb29huICuPuhzhexiqq5vfZoslNRJzRF+ZrHSA1NACYLiWj9oYlDJmdyOU36mq9cUchDjWcgstnO32RwdYK8FCncqvARnpLinqIGGyKOxrFXM4VjkdK0O9Y3E3drRvjuoQjFjtXmEmQVu21t/BGdBaSDhaisYxswug3Ncg/O4d5guw19FPvKPkoHQyJlLtBJQhMblWcCVAvJjywo7Nxdv3CFRT52qJ8yOjJgppMatIwbgd3igAuBcZWrfRDUqyG9rRTaiAi89Z700bfRLT2pydmU+Rukr3qpyfmGqufRPtWGzTIv3qHn/j0lgQgUTX7K1TpL/bdCf5kQY6Eu3a28uUS8XRMyD8NmILwCqn5dJXjT7lIaftjTnR2FsKYJdln1lDL8KkZZNAJPArrw9tL1qoABK/dYw2O/GTe70Sc7lZ4noZvd2mfRhQMBwUiuF/HffDSFIxtHOe9/IIhhCfQL/sdF8bHphdf+mjhVdJkFOezSDlt40GHwevXkEQv3FK6txMgsDLV17/lPh9yccrLA9FTnDYnjEFwZJnc7JrVhekdU6CqLUyXBbiCh7gomJZn1hcTdFmy3J+JNmqyDWxcwyMadvVUaCcW7BJU4VtGDsWi0QDb/dWc5hNqFe93de6jhFwAHFz1ewtIkDcZNcjEEu2xBpvnys2lOPuRp22xqKaFOEcfCkIurw62X+/jiYJ6B/e3YjNVq2KMatU2VE42bI9dnWTFS/oDL6pkZqSN/NcH5esVMkMjzsE98FbPk5HH3R5eZbdh2xzxo4NF9qQMy5bsfj3F912kBVD2FwR3S9RPpixM8VHygJEYTuWoY7IhMkAD1vanMH0HGqhtkI/IneDQdXEzh7UE3pG71/Vo9pf1e4cgva/HdnevwnkN9iZJHwcaHCwsIm7eUTZC5gXR76fky762SWJrn/JsOmduLE2rSXQE7mnFEHzAg9LsPqYXlqGBQDMSyph1s6lhcbJtdKbucsHskif/4L4edaUkA+apoIjR0kEUuGJOOe+QDyIy2f71quIIKy9By4GktHIagcMn6GHX574/g2fqxMw76O7q88ZfDZbSN8/B60UjRb3cCari2fl+0/mFltVuB8hvWDcBoCsl55TgjfXgnG830h45pY2sVwcVPbfGq/8Y4ZPdHsUyHFqi1N5o7qNdcl5eUl5wXPFsqiYlfZbAAnL0FIUx4+E+uDBIofyGtdgWymk24r9zb7BnwUY3GuuA3jcrcNFPQtvSSmts75x9yDZecOFrTiWaHQrDw2fnQ8SrgLpD8+lnBEKmX82+osYAtaeXzJMrnwjd1LqUiNL+icPXKpjyWHN7zPYvW1YtobdR9REaEKckDTY9qurGiVE3b5CRQ9OrDrPLf/rlfCAgXpH1MxdSxmCsikJtv2jhSCrnTJbIKrt+GGDnYBqqthQf7X/trzamBBcDAJ5IAfwrlasoSPyKx1v6aO8uA0X/bitknXRPUTNP4xtQ34jFUJ7BDS4/i5hDLfygz9eAURyfqTMDKZSZr32g1PBfHWuv4sx1tGKuNqRpqMStNFbfSFZ2v/Y5HyVmyNTBlCh9STNGbDohxw/YF/6Hn5IGR4aPFIBoIHuuJI+WNVAhT75dWi6ojQYv7cGRB/ssPxkfjJ8JrTD3OGD08+ctXCU6524NFPVNUG7BWKUUnlOsP4mLawpIrfnF1ai3yp6Sybin4ynMF4jxd0+n+wUHCaA0p+rJhdjv0yfRCV5ry3fqBg2rkD2wGyh4Ge911UxarLTKnD5EagUYGNZ1X61vJAzBU0JbbxdEwtg1lr4qM+4L7KiWm76FzoEclrk7ZnTVEujJLXQDTLqnUIKNDm4Y4LOC9IEYXBBJ2q60ewCIdtDUHxJMAj053Ldrcf0DugFyk1C9ysTD9+JF1aLGmrU2hydlEzW/AT5nHs2zUU43+OcvVUtA2e0wACR0RpuL6z68xSCEWfjYJdF+YIw8R4Z+KkWat+HXj8yynza0wmJBIccFqhI9/uWzOzX+OQczMk8WU+UyutdE8/ISXYC1x2ZYcE0OXGy7eKMl5CYWFV5puI0P6uELgG6mbyzF2u8P6LCuEnsPCjKaHBtJQrnr1zYEQkZig8Ot6aiO75dea+tqCOj/VhVosW1V5irTznvrpZUEwD8/dXEd9m9xOursgOQuNKgHsaCi7uwlSXoPbJ97nyZBQPQuc8AZhFhjuv/CURh5XXe7IMyn+wgxxIOgUiQzeYJ+xISX0fbd+e3KJ659BC8V+gdy11b1F8RzfE38g2T/Sn7gRmu1oUAzAfO228aWEwIT3OrB7KMHjJP6xhs6AslTq6NUwc11mAE3DOFbgnry1xmg94qpDWnNnjQab7nECFiRnRpykpbAgLzC20dsHrtt2/Hgb0qOqRVT/y1sSzI5WchaXGGD1o5MKI6aEFdcG4/tq9AW0Y29ffb0WlylviPY30063R1x8bLF6A/Ac9Ve0mXTmIWAs4ZRNd1mRlBCQe9MgncTI8Ly7uvx3BJeVZlyzFHpXQw4PX2SzzJkX7lSJ2s9osvTDAF+jxFgEPRD0wmYWSZVlwl7MM5X2xkLF0X/xelH5y+3VrWKs7H23XsGBjwjd7PABO5CZTa7MuzVKOZA+wz+fZOzi0y0KufK5cr8vVcbKBULtZwn3kvDUFM8GycQp8/R7nAyYQ+S1BUyr5lIKC5ySCuMr/fay+VKm9OdG3Sz/jrmIf8f765YZTEhrd/G5Q3JnmSXV0t0RgLmb5iF1aw/Kvqb784i77+P15jn/2pj98wW4C3C3jLh3ZF6Pph9k8YAkSfBxOjPAL+lklvNH0DTuxPH3vc5VxTSvcLU8IAYNqDyRZE29np8kT2pm+yg5/aeo2t0g/eDBz2mJtRW8a7P4U2Pb5+eIDeeCuhh0MM1J0r6yXiGW+usUcKpEqWPiWhG1Wv5YIPeD4heoCj4W9OPb0IOupZJt0yzPrS2U/xtBzk/nmrT9iMila2yVWFro6+/4ZVjQFEnxjRwDZNcupJ9B6I87FPmq/v8MqqbNvH7SHyNPLQk6ijLuqvURqx9GcwD8yKYftXIXqN8jjM5H+ymsx1FzdUPpVdVx6NJSbLPoZcd3pfDelpsxqPtrrH6CCtI1td1aaqdoomCq9l1TSFHexIID+56oiyC+5tuR4DGPacydaUn5pWeM2KZcXhnkigQdnJbo8ayi4rw7cSOwQ5NAvOR4pH8Wdm9C5u8R8i1Dcg9ih1pvLhq70ooPqj2RycOlYBUdPNWyHmr62GkEqxeIvWS0MUCc2yp+TSq37Py2O+7vAbSHVJ964Hls0ZgpjKYVz3VIzqICOmwraPSetm5u5HG4Hudg0NWO93j7Sff8I9oU9Gh7FASYmNCCrN09oNy3YaQ4TFSA3JEeW5/XDvA3i54HdmtI4GxXA21QQsgWwPKvp3GP+EDO0f1yASUtix+TYGjLOD+Jj8kuVD1+qRaNqvSON/8qSRZZd4e4eiDwd4RcAIgrmqjqcnjJl6ikR2IuxhBHb36enbIoBB3ismPFf10tyPScZKd+xkqmT3IFR3Q8Nleft1LSS+61pTPVKN2MgkCe1cHvLjlGDgORUVsv/Dw0FYZ0QFN9CTs9HFn9bZ8YkBIPqf6DdkGTVvgZzelLQQpeETq7NhF2tlzn1tDmTLPDOBk/7k3xJSUARXBfmyxTk4P7HxT/dcy1Y2lT0mcwp2w2PHz7NoKuRWIYRKHqwXLtmNNxvx9b25CGqUQ9Ae+wQJx6xoQYFgIjfGkpHBsrEmYu/Xvekq0J62ojhIqp3waG7Q3Hq2+sX5eZ0goc8/6gZcbvjVgDXqzvE+THIAM4HiEHoXEtENl82/Bk/f2Dssnfmcv+mdZ4iXcUoqNk7ogp7EclJvFyEcqtG5Dsc5tcg8dAwdp2IT64LQw8zsSDf8aYnKGMl6AikFup9c4W8G4kJ+4UjU3fOpBuc4JlOc90VHTRJVhsHjFCg1XOSNNs+DWzdKfQtV/AelyK9kGXx93WodW2BHb2jX8rvOO7yQDVvcq11Z1PXkkLw4RiIB4WHZzGxRTBWxOE6SqYOYW14wcp5jjfXz32Cg1Fk1V6DVbsjAYAtHmlUtswMG2dco8x3/2ILGth9zrYI4I5YRtG/qIhG7n2LaKe1avbybdaY/23sHpTqTLYnH8LtS/91jlBgP4Z4u1LUd+VRnNh/3v1RuEJvr/SeySDm5I35f76qw64Svb/u6Zv9SMUz3IWvlu0H0P8SL3XC5KGq26OlFBdzuG1XUvdS/8G9d7rWp05f7ho7icYGpx9iVZIIWm9ryBr0oPUfJ46fK6JBGzPiYR6FJa9UQ3Rrv1Hjw9pHqAeyYaiv02dBHI3zwBszfWiM0RzD4i5fN05HkHHTY8hRQEfF80jQISLmzgMKA/EgQY4kmRZffUWUlsP0ZXD15ZNEQjtABkxBV5VzZpPYu7kK4D7meAXyhsRfJL+OAJ6aaDQiWHlYdyzuUePd7bIpL8wfXhwFfgXilSMCPC7bnHAfOVMEWAhivTbN32UFidkZ+2gwQduLGx0YcRO5JNXxE518POe4n4L5EtuwVnOE/LQpsA0Zw42ivNSjAt3/D3QIMRXc4UK7TDdseg8oX7igGcLkW12JMDudYTWCt4UDUtUJWqO2YnTxmZNpO96wzIIYx9cvDrjKkqU9TMb24fOeHWsk/kYamQZwPIRRtdFczgjdhUj3U/r2QAjUlB1DinTKTjqncgerYQbA6qTMNgD/vTe9plCSorm4l7Tj5s2/PqTXbks/0rlH38KweeMaadnlIxdOz6w4JYAbNmEU6vleYgzGFOI3wUJA9ebWQAYksNh3l4V2LY13qItW1jAQGK4yoRZOV/tus/bSWntJ+8+GNq+lxcYLpJlwsr60g8+3eqOiUU5K+jcgS/FZtcOA/Sx9mO+cMQjJqaBrf5HTNeewk5q7yJLU3fH5s/bnaCtBaeZoRld8QTaEw6FBYllelH0jfXPObwXmVIq2+d8f6gViZ2JIVctHOSanwjxo9a5OPCoqrP40T9gERIBqziTO4hZuBJI4UZ4uxn7Q6OGZTm5NmUqmc3SlJWW3NVZXa7rDl/x++lQ+oXvCxLN6Uj3hvmUxlBxG/6NMFChiSihS4jkAZ13BIxkCwmcgTKQNYu7qt7JeZPXUtjsYpkSAC9EWzlVI/kNSkNJVmLrBwjs6Rscrjt4MNUlUoWGpRj0RKzDdQRMjrKWQ6D7CNKjZVMEv2JbnySsMWYP72q0nSY0YUNKU/q2FS3vCYN00wKsnSx7y74fXnCoZe6vzLyyG2/QRjAv/1u1n/ppHU/AIhJz4/ZvRBtR03yHH4digOMcr25O32L5mGaAyfb6VDdr8eSRw2hJLMqIPG8l+d/Si8J9eHDGAXftfbWoWaJxJ9wDEDQgeAlRvewK5yzi/1lXu4tX/02oMd8CTgFjnoJnB0Yq87HE6NLLWk/cjAs33PQPMudfJ7A2sfcgeVVK/4dHcd3hGP2ROUdiF5LfaBqDxDbJj1ledaowW7jCJRyd1R5xBMPHKEmdM9he60nb5p+dstNqOgCM6UXsxZM6yYw97ZJrSLJRwpdmOKoJ3IrksmVzKTxutdUackfRdcxFvl8U1E5KSSGpszHGFv504ZwN6yNA9IfkF3N3zrtET/Gc1ahdsBW1rGzwaE5pPzXUrXu6w4nvnCADScYDMGieujkpAmkTVhYZP4nGgLJrZ8e8iN30455LpgTYVLLffIgJHqhfyooYa1oC6fz3aB7/tgkcH7E3iyR0OenKw8yvHWugAP2evwbb9bpoa3N1XvPopcUyzhdghUvZRLtEdTpnPL6zYChl61oHDSVqJOpre6gcCdKMqEBcE/qI9N0nrYc91+kL2Y0VVfvZc/lqNOsh41SgPsJ8L0pfYG6nunShs/1b8OBnYdVwX6yE/4W+QGIuT+2mTuqnHGpsvV7V+uw9SDbSV/3W1BpVpjk3NzVSa5ggwi+Bl2ApVrvLl0iGM1ECVjkIX/RjDkFKQwOdzoFK3OUACLMTfzGBwNQB2ozoBTD+dK/SEGg1a3d1zgl/BdK50iFrBKvbKd9hmeVkbcKGPOfyG0hP9t9KhB8904w7kFLtqT3XiVwJwz10le6dTcnlxX6My+gkl1Vv/3olKORfxBb0oB5C0gOCvPohPMEvMSNknvTNKBRdmKCpgQ8oM0iOkFeOxKtfdlZVXPJW5JU5D4UaDmPSMzmqYFhZ/SJFrg5GpIbNRHFI/YozaT54qzFSzwD/A6+2bIPY2v3HAasz/RUqJ4CWouhFtqY9RBMkosQX7NFVG+rYzqdWhxh6/g1PgN2njn0UbmHDmBQiqidakQT/fKKIkuYhqw9f48lrKfbqqixmBOu5uFK8ZJE8tqGqmIsc4UI22h1p+pAisP0Bep5dka9HE9bX0X7A1WVer5dBb+mFz1bLXH1rR+2W7t0/r0KeBQ1XRZEO/c7jvsRP63NJBbRg8IKQI5AxdUVlslnMbzCfIGaooWRaNyh16Wsc4QRPWzXfiKbl4Y+lkWLsz1TbI+flLy6lB3iFZA4NMTTzkhY1DamInbcqz08OkyEeLASwc3UyafTudnBGmnBXutqylDYAapDO0myioDkjjHYeQ5sJx37HTfy2RT7ddwSer8vmMzk59s2o4Rtb68bAz93iuzh66SBF/9kCs1rjP9gxHxGW5cfXSaZCTMCERdHtiOTKb2EC0fPROEMjp0qvU0yPb2MOSzGW6FF4wAvZk74vrvukBb+v+Megidk0LSIndzocANPf+ADrHPB0Z4K6/9rCMc3tyNBEKuHI7Y5CYbH91WOY8/tJWTYdm4p3I8ISliDKOSvUN6YmPR0DEegwcggD1OCMSYdMwHzeY0cjglwYKyZdBlb7XDILilyB1HyDQ5JiZRs2Dh5FKBMzRQlQVBM/MFwgkbaS/82hgEfJyhEGFRmGgY0+9FdJYlC4ITM7GUAdkYxRR62Sw1aGYUDDWPvTFgUERbMygDUXcG3hAH+ro+hwJKE0Mre7cZ0FWqEztWjrtQFdioiLxdQDdBSzf+6utsFCsbxLKll9LR60z7cxrkA2CRPVzzmLZU3APCLO0mmucUXd1mwc2EDE1JvAHs6n7x5HTK0gbKyJs4jCRBj9wP7weeiii2G3zfR1XWX/RF2juekt3In6SOkUoX3sZCnMQyjLteUfqsHSq0mT1HSA35EmGQYv7Dxu7BhgSFN6JGAtncjd1c63zmfps9ZCEeSUWVLrSSzlyjrSPtop5B0ZnB1tXJr+lJNbE8tWM6pvnjwkO06710goU2T2a9JDOxdVd/mmEiRf1S6tF9bRPwS5kKBCwP9M6jfy3xUpNmzIE6bFZ4qB6a1ZADYw5YNz9s9r0OndA+f5BZUlMmWjNqqmduJO7zgZ+Ssd3YmoIdnoMHlClqA4827xPlCWMBNGV0t/V/7dBvQX4u1Gnva0G8AzWRWkHv1ZtNTT23kd5+38F6ejfYkZLYK+ZDVKs1bgI+saYxD+FmkEyecwrho7cdzwUfv/K9K9H+KrQQ8251bs2fBj70HbNXMRzpfAgCVGTEMYgaz4vMFCFtrXuf/wW1KneIIi2X5/4Xwkm0L3q5bRrjbsmPcCjLvvaWEo9jBu9X56CFNM8INcWcFMs3zqr02cnqRdWivibb8eSzOzxNlJbUy6XrAG2TC92/n1Po7lYaAEKNovoym/hPtoXUUsFPJ9SsQs8L7T2dxIAt3cUnIp3UH5K6yJrpie9CtNNPWaG0j5A8ZUmJdLJll0wzWl/qcIl2dGeeY4ymLHt47QNwWRX1V6i2T9NERPerHX98NqC2EFhbuTAbOZXMlVd2IoYvtSDQivL+9e60TFqhyVDAFIC2d2rjrVY5dAQPpdBOfgejtu7AhgS9iqKvXZpgzKdrSgEaxI96msCIS1u1u2mgF3zuT9toAAh173x5sL3isJ1N/tTfg26yoDB7jx8FGLcVa2VTn6qAElbydgfAQtQtV+hZJOPgWBSAXUWRiUzHM4qGNXg6DmbTsKXdBbXOdo/6NCV0K2LXxarCCMH0WuzpIRElB0ctcU1mGGoF3N0Ef90kTqySc756iw1gcUk8W3nljADS6GrDRGzmtb1qAR5oJlxv3anJZ2pqfARIng7+UuxXKIqtPBfA/vUnAwUwZLoO3cLOSK2wrj+Zp0wAYnqwb2qAX3lyKKXsAZNheoF371IyDHbHz5VL8yPMvBwhODW5gUw0u/7VEPBu6QJlUSKsfXXuvO6IIcZ/xNXhtWRA1CM2RZE2edOXjOnp7ivDcBzRRh/QI0nknVlfBsLZEROH7IUkupYlp33t1EITNQT2Zpz36BQu1/CiCVC1olxi5ts3vliWYN+7OZFR/+xqCZ4xe10woZpBtHa6B9IXhjHT9pEWU84RYXfgSa6iHu6P16z8mwo7Ont2YxJ1CGmzgnu05lGHV+NMR9YU0uTEO8EUZu0B3GbOBVQay8hj+o2RTEOvhxEE9zqKYFrTLZIqdWwne3nEXYYuR4FGBWdDG04CgZ5vPzPhMXFQmOPDohkF1ny9KXBD61VpISWniI5ooO6MsRGBpJUmQbP9fl2pI5V0Mr1oEP6YavMdOLTXtWtj+EJOUkYrijTtW+Dgx+QPKp5Y7Z7lAbIrHe/SZyfe+VwN7X1VnvKCbf+mYskH9WtikPACr3W3kAMVZbEhxQYbCIcFi1G3ryQBdFbFZTD0PLqSosqR2zz9J+LvKJWnGq6nSPZ+yk795eJAz3WD2hdELDplgd3p9qcQAQ3oXWZR+GyS9FP3zpM/l83kcEDvXK3E0NgcfD5dbViTsFQxToUhr1psHQjGdFd2CeUqWFubwRzpOS3jg8MKnu1l2INGIzSvv4x9dodjQR1v9SikTiutBMm10a82eeDBU8ML0xwPxgePCwuYGpkGiHv92wcer2ER2l82EAdVhRLftalMXRpvoHD8wPYcwt3Swk8U+CgGAPJISodqcQUlvCMMeE5GVSFGs21rh7Q/k8AeqQuKGvDdOixfT9CmhWh/gv4f9dsT3jnX0DjKeRL6Mo10ndmzjgjA85kl5zY2Al8mn8TC2vQsJFlIPpMiooaDT3MLLCXMMKZIBkn8qX0mZw2MgEj0noM45c/4w4pkwpfzyLc2Iu8T2CsHD9nSK0+3Pk6ei4BoJEbh2c7TdjQqcCF3TOiE5sUDeEOmOK4ArQx2QOQ1mhVP+XXHQ4/4DgHz8QFMK0kpsba2o5Zob9AobYJhnwjGRXM+vpR0+aA1vvzzqUvdVoM+uiR5H8/nTV7Kh1voPmsRlT8SolOIQZ/p3zLO8PXnrmhwMHJP4TM5Ad2S8BlYq50/4fdijRBRby11w3k8r7LbM8TguSMYpuTsqs8448YVT5uFZ0EyFdBO/NX5R1FzEBS13uuyaKqkMHbuyu+umE5Yo5OWIemUQ7MjI+oZBEai1HGNHorAobDgJyDK0ByCR7M0DHaDKdFGYT3qf0P+6rtP0lv2NEefX9S63dOTNDvBJXzR9hvUGs0GpohCzec+W+YQ85gYs+VMf7sYcssyO1ss8WO27jFTK+etJmGa6UmGxTpjpka7YWTo0CvF3JkKqLutjchxMD+XuShjq9IsHAgaguyBRnVUQ1D2fMDJrP5kQKY5Jm/opFS0E1KbAaLv0dm0GwsxVrLblZM/YL8W/I/AIrIdJ/yEwr1rfO1WZNwaNG9X9/40bOfJG/euUA8jY9kuODxsvq3TgLoGyimRaqMpzbYoP/wCjU1FJ4wkJoGgLfhloYXJ1mtnclMrP1kxxMAVtj5NT190HLlt7IHWlQ1jiDr2Poj7XuzVgzt9BKs665MewJAmEpJbKsJcUPZ/XOPGCNc8LiaUnvZg9yAf9jLRUXZ7nmQjb+sSYY4RiygCKL2mMY7mBnJzlQG+PcW21GmkUrW4RQpm1c7I45cot/vXwnsHKL0isPfKoDb1dOEMi6XfU6GkQHi/Qsyz2179f22rH0gAS3t8ePJIEXkxXzCmJotdlpbZ9j5WnxkUXTq78cKL/JBsQkQ77CAbe+X9RiMIb9a2ieleA00Hgkj0Ep6NCiSO54hPafQeZMZIOhqgjhFKGGfsWURREudzXQJ2gx0H7o7tmPSaonXogTnu2sUTrH9/W4xIMvmP3o9BOoKGDJWFjXO5u6gyWmqqNf+SoEFGr9f1G7qYP/X0aSBL6PqoS3UirOeWvOxL+3qMAvAPmG9hUJ95KLNo9PQs4R9usOSSPyYGJ1+fS3WuBXXy7oD9wlf80GYXL/wiVuU+6VizxMvIiqRLC/K+GW+Tm5++KjhzhIs87EjsUUCYHvoBwmR/Ss0fVWq2fD4xu0mgWqqJHofiY4LiJcXWwK13yVgaXetCA+pUZ4JMP3HolrHSuaL4CtjoJoSaYeM8iAp3+CzXjBtR/V1GuJYHgLONEehXAo0L0Z7ipFv0cQbGB5ss8HbLlaampla0EOd0PVirgF5KFO19SOb3LQPj0LVJPVspkWGYvy0V3Hn6Uo1EF8qdH6yktcNhXLF7ehv0Td+Pjy6uv9A79XK/OoEI5lvDXWX3JWgfEBeXufeZc30Lh+Mz794HJD5Z8nlaMRWPSofNMzOTN1q7pBk/+0iHyi5dwO/t1dtVRlW/FpAkfOIiRmVRefrqraRaD8bqBeFNRmA3G9WwLwJir5pkIrcSnQcKbqqnx3gC+Z3rjJCW4brbBeyiqREhohWznSSNWWAcTH8nQEJB/6+XU0f9mHG/Wir4tDXDMOSf2hcWBzz8F286Jy+VFuaqSQ8jpGk61Tv3YpwjyhLIv07YIwol+LYnIt0cQHH0ySeU/ymi3tZGI3394S2+LioZtoAqf+ZvOfFogrfUA25e+Dmxz6PPEtIvpNpmP5Rbh6KGooommdujSBeve6Z+Bk+0l3t9TxXP0ZhcsE7ov7XzktJ1VZnc+HGQeDLp/XtuOV6VFL/0ZVmz+bnOm3MzcEXRtVy3ywg+TOsOdzJNwXIP34t2Rz8mwNQgvfH8L6l577TEbiaqYVMcr5jCvkgDiF/JUxXqFRFyfWlxVOKd8AR1PzZXLh9GSYbv85uxAjSSIuBp5H91dbBdE8c+BHchfZhJDSIxBX/Izx9P5vmxABTH85F6Trx9MlKUlT9JYczm0kZGY7l3eI99njj4ePOnkfuKumHU64rSe38nQ6awbKOUPIRyBo9H7eOgZ7h3lqbHEdMaO0OmDovXgw+4y00/NyNAM8j4nScWztlGP4XySzuV7d8BmiUg8eC5syRR+xA9O3hILausetAebfAgg8b1Z5r4Rl4Rmqv/BEje486MkXFoCMzj3Atkl4Bnvnl+fE0Y3ggk2HTd3a/J4VC9LWH1Kyi5NcH4suJeojPa5sCvmZNjNeWudaUxG+Lnnc2N45msq4lVfkk2lWpurctyNLyaCLUrYiG80HmxBZaU1Ukq68ideUgdzqmQsR9uRfIWPSr2+zCQIgZppr8l5u0QyCLte0CwyxKvfAt8jLBmeEr7brNE8YDOTX0hCaGeyogOWEIbstMTcAIsPEbmTGCOT/GOphHTIy6ZMn5gESI8L8Zhz3eh5GNaQlNoMv10QLVK1zMmE9ujYoriuNA8YvVsKDynHxvZqQ+lJPAJsIVhTTrpCqKURzjlAVRYhNlrkeWo5WOX6ER12yXq5EslGSRHWIeOGfb2ZC9seY3r7RqENN3dECpDAMHF5qrSGzRpr6S8fjei2gRkp00sDbt+euGC6Lrh17LHd/O2owW/ffL03Lgo2Ys67iQOUHKh4eM6qp/A602COeFGluqN6u178MR0pS62bwtMfXSyAuD/LDx8kfcDqnUwGWrYidTH87DxnwNCgtVmvNpGNmw8wgLFEZP69iXa6wyAnuixC82VM3olJVPHyZrLzZSP3yuDHOcCk2z1t+0AvOzROA7uSf1Xfug8KTxEB04yMu6qJIC4cirPZ5nDjU0W/s3FPaA7Wp/EvCEV5k8Vs/s5GV3namw42LkpCfEU28osCuonZKGspdvOoKzQpFuIRN4NcWIiLvemOTyHfIFabTSJga/G/JTMaeRP6z/SOR8J0MpCM64NAVm+K2bTk65fJLh5aaX4dguKe1NKf3Ih3CHdzSMUJRq85rbC6tUYPfDM1PB+dz6vzdLIk8cfhuiPg2xFlRtAkeHZVl0vXdgrv40knPioQXz7iS6lDgii3X4Zm2sPNe+Y2825UGyK4BKKHm3YmuCEEoM0qcf2cJGkLcAoQD/uTqb9nbJNvLvg79ITMvH/GJBj6hxblA80u/c3Nk77rRsMpsZRaJ2LCmVcnMaLd/L9IgHuMDYjqNDzyxsHtvykilY7cwFz072U8PEWenM9+Zd5UPAjamZ3L1cmO6TzifG27/eGNhRaut+P5o+K6mrSmpxp6L6KlgU25/XQ5GiEqj3dzm2Ew+jIvKolJHSuLxoyTIA5EFbdpRp/PIyaqwyZzcmSzKBrrRUPbXKTS0h4rnKMc+q6AlQ/UL/RScYuyNJ8REkQyoRaJUcgKTF9A5GK/XSGWiF33axg2eFKC67sKKG3plsxX7P3gEAkXch9Gz1C1QiD40E0EhP/FPzeVYKbkFRxryuxccvVqdkH2OCqJBL9GLm0rkoaKOUmV9szZmuEP8zW57+gg0d4CUw8vRzj2a5tNzsembRSPALsKHgk2VxoQe6LOygctY6/10FF7V5Twiabfh9p4wnm5QJv11h3V3VpEPcR/4pNhRD31x3pUgQtKipCKisNUxj0m5MhMUvDyQaqDjpvUapIqhKnCC6uuONSneQa4KfN18cqRJkMT4uhDCK5lkXMpsekGArcEKaPfnT9qGiXKD6bn4M+pAIkXbdcXxFzcpGbBdvpIrY8+HlYBXjA9+3zxzmvgFxylRkFaChK+nLHPNwkRVSmWPNI/G3cXeualq+/EW4vLjiuD+JUycKw9HsNfDMQKP0c+72Ccy+EXdDsJXXwPeBmMWR5uvOcTQ1JVoIGrr7J37eJgaqJeq6htVqJawgQSzKQl0K7KrbfFdvJcPZwctunCcfKxIlKM/CeLf92StPBQ3n+0yi84u6qE7R885Fuh4FwhDoS5Rx2I69SY6cY5HDueEuWRkca1Ag5JQTYoFme2Xex3yr4jWhxLU2FvlxqaVMEiJy5iyPHj5ykrkNW8ueYjg7AWAEXbCjwN+7KDPQBwJLPYrbMBBwE1YTN1TiBlOcfLSbP/8dZtSvdqn0SlhY/bej5OTnEuZHCXdJkoWIbZkH/pkbp1RoCjY+3p37D6hihrRlq/Gsd0WBaL1Ei8jmgbQ4WJH8jCDh36XOUcPkXQdVWWIepRKF1DGy+BbQN36SHQQ+ARevnOvr3TwzoTqr8GUguyXoOxMmnRM4ZlFNAUNVzp9FW0lU51W2IkoGKrA5NaMbsUtL+2PnaJ30AnoWQq6KS4nPT6N73LNqZIYYOY9SG6+ZWdb1ymR+6sdu+Du6bVv8JOJMKZPyamMDYNJZMvZ7+ELxuFBrnWxnyAEJUt4+QknwAxBPIAFXxU/iLh6l+UzAJ1INfVdnw6WXwJAF7gffbCr6pfK7OJVt+jF7CciCRapPuJYMusD8Igx7styFCW/3sBgs/xHNtInv6vjgXwAdfn769/RMJAGGq+uy1HjUOEw4u8ycPuPbzBpmWDSYy++FMsQ62cbBTsaRAPaYpwQbUbWDY2LSJa0gyVz24AvzRWnfvd+fNF53LNemL7SxYz98nUuotD00sQYYZ8GL9f5YXiBg6ciwGaUPTqUg7YiH9HuSuWePigZ13d7GTzInLZImRWJE/JSnMn5VOhgoNURMpNW9FEbFZEqs989lXA3FR2KIB5pCB97b/1mw2r3tMOcSnCr7miFQDN/snUbF1pG50Cl2aLrXQXdcfQKtGh+eBA1AjYv8UlnbKPXCI1B4L9oJIAlhKRXmmxOj0SlKvYZ1QmEV5VTqpeUmfhnxK//AO3GiT5rb7+HaJQ1+dZqS66lSJfHJSnsqq+rWjJoPZwhHIG67Dro2enPlqZkFpX8Hpl/IIOjUQD8t1CeMt5BVMDW/mcFXvollPz60s+WPpfOEu4BVB0Ck+3+rfcizxrbUt3k/aN1eHpjRfN0jrXQqYGPIZfDmfYuGOHL074YJ++Pr7HheAJqzUUZdaxsoHLPNIfrUcKL9mvG8xyM+nvQq/ZOI3DX/WcvVwBUgBAHRFfVyYJh3FnDWdtMvurfiZva8RA1zxFOtvMZiPp5yHmpspSaFiWpLBF3KbgLeoMnGynZUwdMadrgJdHmz2aH1lMgL6sNzPC0xsQnUzjnp0FUcujI/nAi9ZHPdn4pAsacvsC9yinQN8lK9gWEEo94V5fwF/rMMrD3yrtoV6MxDcgrsJa6RSVmKm5nOhlULrnrlyYzS5XERRhOHO0POn7KYtWGLAigs8R5qp6p8/K9SqieK7alBCkSxD0ibF9h1yY6yEj3VHIdfBSBiyPzJm94clRgAN6IdzoeYeqwe/J4kssslBO+wUx1jH7mUjVQl0sMG2WiQOQnIF+pRT2N35hBkMb2Tu/FDG4wFaH/Zas2MXhS2Qinn4WAb1U+t9zUMMN6ma+LrwWO4VCpqowgKdKluVvBSJoTf1/si09oqHxYpixuZcwJ/AEUM+Se5OqymcjzuyhONBa8UOQnwfcaFXiaITdlhBYSQvfZI//zwBfWD+VC7sGlCvfOb7BgfYgjextpthBwVrKxf696SCFAxNu8HwS5tmdG5V08LyUrLO5nBn0VsA5QjVz4MEE=\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" ->","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"LElKq9fY8CyU9HssDOv33EsHiFJrUknrBQonL6f33JXXDPb1TUnwO+uhz+quQp+kdfc4tm4gQRMxLoG7k8tHFAtj/17/bnj45NO8JAGgtUneVlpSpCQopg4Awtn8FC5hP52gqkFuIHosXKicHP3eyGx8bX7IdRj37bQouPqOtPpORVozRhJD469V4b1/cTFpss5gEMB/zRShsBrcMS/ooPtEkZnk8eMxrmgpQD/SzX2LkULVrMkr9EKvjwLhaxxYHngnkA5d40MPu01dsafCGymOOwTyDuVo40N3rs9LmowVAR08RR+OjnaqKv+yoQtAi6Dj2vyXMDWF0OBXsqDo2MDAsb4OEETRN+IaKFNPoRc55WxwxL1SL0Wucvd2dsSAB70pZmPp7qMy9VP8oLCR1S21RwrJxeHQdgw/7bDfJNijr1NVzkDrBhI1K5RpZl++Z1/fTP3GqH01ESV/XN88rXjmtZ6XJGhteRr/t5B66Qe4BC7oQKl+LvIeUUKFUX0nIJJkMmjuUXl2/uBvz0mdlipbCLxfNmbAkxge3sSsLZ9C8vtPx/lC1GL08TGu/BhEB1ru5VeuuTVlH+8fWfuhukr8iAE6wKlPjuyBMyp7vS6+wvU+Dadz/OBJIraI1G6mNV6ze12SIDgRQu5XAWdhdhLnkDc3lVFkCn09bMGyGwzkSRFpfcCj14iqZ+WW/RgA4FymCxQyVnKL7jl81LvKIVC9ChMBU8pt/jzD5gqe2CsE4wB9+dZUscXg1yAvZo/e27TdNYDSyUwiG589ja8plRtqGxImAIQruQh6pZ558RKSqAbw1KYsJqLiP7kxpu/FlTbqTzSRdpXiPeWSSJGTfrW3HqbDaRE2KY6T4Y43lEKB3tG+cQKgHx7Cl1V1fH7YieRcQoBbGQvuhATha9S0NQnztmcgaCKM88v9Sys7Z1KmteatGeHNZ/qwhMfUjWQPCAQedTskRIwd7UpqcqMq8L1vL2DYquRZ+z5urQiC6YISJtSBVqH7thMlQPnp6rao0Ffw6Si/qFtfZMo7lrDWHtKpUz+M/4HztXUr4hieN1gI7XtsTikDpQQVOYG5Xj+s0BoHElJk9WXZlilBB5JvQpoTjS1zyA2klMtQukSxnCIiLfN5PxkKl2LLYItfFnpfQ21c6x68adaXsxQw3/2UUNLghUEFFtg+LRpdI8nG5JF3d+lhFTpx0VVaqeGUhldGRvkq14KATbpn1opnnJ6a64IYOJP0Or23ao9qDY5jnnXexJUtimOeTWz0xT1y3Q4d0IiV8TLC6lkWnueRgnoO3n45JIQQHQpzn7pe9w9rzoMQvo3rlo8veNsbDTRmFiNm0GMOWwdGlR08qzxYl0EvNfavTGtXxYDqPf4N7GITN2jj+6BCk144gH+z1HlklevNkQqhsguHreqe19x3j7X6KlK4GdP5WQujHTiixnVZtB7mx+b4Hdka0C8fHDl6Py8zzkQTvYvs064yF7H7fCPlXrfuFMwczM8VGVa4ayElG37/tmQwnl0jICE6ZJLXJS1Rkbfg3SkPl1Re3vZiVSRCpjFcETjkBzsQzeUyLSHTHy8YvH4pkxyM84jVFco0heyJpOzlP0gI039n4NZQSWvG4xpsDT+VORtZN4hbZZQ1IrXiaeOKg0quEBw9gcYmywDDhfpnrIr8Iwne+FueTXD6vXCLCQ6FwufmpUa8V4jMARnobO6gC+/ABxGY/TcSVspozjBg/98mcZRsSkfMNIvKdqtIxeo+YnSNbsSHIGKBmngRusPrs6w4aackcY+gtapGI8vW7a6DSeigr3lB9sPkrk6CnonnT672s19T7veHQETWfb5O9Lp9CP2EG7qFjDIrd6N04k2OLNTCD5k4dwNxSG17IjU8FxSKT4Ffc/Q8jn4P+GFkj7+81+dWZuy0Cl/qJz3pliVxOuAxMU/4zmUL7zdaAFGhQnw5hqfKyVq5rjY12bUbf0oN9ZFv712j4KkxgMTKQ7hDw7ell9xCGCuA5KVpcVSfT4Rwzr5q1mgy0Jgxw4E//KMKjn9qqe/5sXbzIQkdbzy0wEXZBqbHKwtjAx+sxzWDtANGZdAu2QmE8BuhDQSBqot/R3QRzQzx5RuixrePld1lWflWgkaBwLvKcmP9UdnbXPckL2a2+/LFecgksCvVooN8zyiH1W5YXiRWVKH9TE5x2cI2X7SeMRshGUzZV9yfLR/RKMLTe329gsrWLsCcGjxzFpywHhlt7FAIWoiiRCtMU4arzj4kJjKz26/KKCGCEx1OgYD4GqY7IDKOriMOZuSw1XfXrhxKtU4ddfqXI+3gMxSS4Y03mc3yvonwJxzd1qXk0LEOwF6gj8xoXkpcyqoxNr8mSQNPhJeWhIFg1mlyyIRUVa05SIFKld1qc0AMk1n8/tCvzHpCbFScXL6o3o5b1EJetyGxdRmppr+AoovtQ03y0G2Z1uUR23AtbxkiZWlCB+g4Vx5eIm8e8uLw2s/AG6Qa8cPSKnhgJ2b9i3UKpVS2mHezLGTKHNsEVGxjKoAdLDrEaj+S6y10J3RdcQ5r70YuMAQKoT/NqYjiE825I2IvS7A7W6BIMFMuf2sPhCpT2RPtirK/ppC+Z8yGrW9cZFfGeHVTIetLNP4sMCc4R0ZBGcyBEv8gXFxRyQ1WTraIWQMJeMDGSfPwy8kwcammlgVyL53QGU8Yx90hXIiaUX+zbAmHjzk6T+yL/PrD3ed61A/eeyOwcWq0oQk30pvtk4VBeNw9vopFDJPJdoQayQREDTc6u4mGbj8luQlNtwtxaFzKTKjwm8ycMUxzGPyj38UMeuZePcJ3EAeMIAULi/3p0LHKnmJQliIeCMYZ1INd06BRUaH/TxlIICQveurvXIehKny9mq4DDMKStEkXVkMkCg25hlPVkEvHUB24FVjp5emIc2XCpJfN0jUzM59ZK0EepptE/CC5efI6I50BMs/ssxB/5mwDprSwVSHnHlcFZNgCp/ecTyouhS3BxwxfKHFNSEBkTayxz2i7U+GLA9jQ4C6a4oqf8Vq2KM7i7ShwtuBSZvgPWVWl6Vu5epxEatszjkdibEt4HtpcGaZuTdCYR6hUAWYn8IA2BqWS7gojfaJRV/jp0NDYXa9XvEvE9zLPHR3HBQLxEckUYyaoJk4uYeaUbs/ieR594MeMI7BJjaOieu3fXZeZozi+3lREEcDMCIvy7mBUaCBHZNcQFuqhRkRSA9X4CTy/QH0E9KfXkrAchjsQHB8X21Bql2GiWDZ+cthlVnRGmLLQG9/WwwYCrE3/q2T1aBnA6a0GUmH5OcDXdM4hI1NwkjW0hbY3NcleOh9Ad3LQ30Jw+pkEo+cPggLLwjKqUUGwpVM9N7OV8UqobRPP4yR2pi+57NKPBV+0iddQfClw1Uv6LubKBX02uRm/bGCtLqbrGTIlHDJ1aVIa9QoAon0fKfhh7nGAXxO6zIevC0c2UhttpiQEXO22i7orrmEaXYXTZSPKA8Z2BFlUlaHb2WDe/ox4IjQpQ07H+kaYT/Y72j0nrIJ1MYoBQfPToHd1hQG6wMCLZb0l+HwH/FxvlAYwpSj3u0V9D5wmLfVyadoLP4j7b+Xgd/VbR9DXrwsUskOgZEWOOs8CdayrparTSkQFVgYDDaMqgQkNqj1e/0kiXp+RrcFsrNz2r+icUeZ4DHbfrvTB8ENeQwjfpY7e8GmglGtllRAuSPxzuEwx6j6+Gn9hFehSfUNmO0ZPU7on1N1SRE0AqUZSjijRkVxXUkRAxmhg7GIjeP7faySGAGzViWCethRbzThYKDx5kXKjtbebq2wMbaAIamY/5XQsJmXndySfoATjUzR+Hz1ehCFvqWLuPQzr0pyjTindDIWDHwEiG5jn0zJ0NLcYZ9Bc2px+K1+VG+8vpFJPQkFnyLHCpMwNr4ZiOorfYQx6gbLy/AOSCcVEQJkWWLIh4TEWeuLDxYSaZydnwEfLjpjXgbrPi96ZlZcbyULIhTAqg6SFiXQr7W63dnJKzxrwA6QoV3TYQtGwRL2/03yk81+I8waZBFXNLjMABDY/KMc0VznMOU2HWIhHUpkUWcmitO+z2tdSp0K5CBtqFUeQzxHUllSuEj3J4ZLRresIbhf4gXB1eyIKPb0TbPbthVUDG3UVQQrChJ/Jz+E9mGRDQLtN3/I5IA7gqsqII1dzer78KAktdAKEt9S800p6puqQazddq0/Q8Y6ycVjJAEYshQunAjM0ev/TG4j4jjrZjjegNKfUvwKcHzYyNSLNSbqcNRzirL8Q1cGDANFsj4wl+lmggCD7p9G++uwtsWZ+iSkjzr8Q++A4zFwNB+WzxLlfSuQ12eXV0tEXTm1MW4rZ3IQO13DxP1s5m/TgjC7g5NlsdUrprBqvxSeK9ThHjRg+TKp2bYAiqaxd31bvbzu/r00QW9U9Tner9nXn7trZDnbfGRWRypXPCGTTH4u39vgO79x7mAglMMRV5txmFcgcgGAtPHF3Fri7C9vZfzNFAUBc8AWrAoVKrXlJ1bSelEu+j91ixD5OqZgP6upPQLTgASGArpU4AK40TUDQnSl4uy4nbTLN8RQKa5RJYvIvGXOiFao/1QYczOZWAS21BiEBcvmqfN7/MsjcUwjwMTgt6XfSGbGGukP+tusVeHuKA2XvbBULQfYZpIiL8fxrOKvYBTfhtRQB8IJdrYHj/+eMn0JbewYMQ+xrk2eOYqxTMtImotpuA8afG7Xu/0XkyCVBU1ZjFkpARSRYVtl1Se8RWMRzb7nyU0nmOV5GS0VdupQ8Qz02RijQWDtp/rAwlHpFBT6PfnnGM14N9oFpTc7wLrLXa4W5qlfpGeuinvmP54D7wqSr9G0ITNqFoilyPnQvDGCv2wnD8JN1koyOAT2O6SFrkdyTrD6NBVG82kOdPCBpDk5IYx0J5Xq0L8dIw596TwK1khVAcdBG4z0bISDg/ClFBhVrwHid3QX4kKJZA13V5B5tishx+6syMnkmowtM6w9jQRSxjaGFJu5fDC2SHxMHzsAI0bArNl01J8c3jZgCSfn/PoZy8fmBtMuYwsvRxRUFNmVKGfGnvcdApqVE9Fo8d9iHK/tM3UOSlmKQpeb5j7Komnx2O/By2RUzKvSCoJ2eKoRIDlcSQPJvRM9I8Hz74st96mmGwmhIqqkrrZ4Qfe+svK5mFcsQhRQg8vX3vNXifRa8Af/uuGksDfv+QtrLjGnZscmMp9H3lJmO++jyUH6Vy256tDjrSUjK2RBatgt1FJaPdYpQL/OfgzpMD4D980WvC4R3HIDpdtd2mRP2ZttmjBhAKwsbnAnX/+Axvg3eVnUdB8SDPmKLPibT2t44SW2WNZkIwhTH6CD0Wj2xzntFsBp9GJW7gAWWAymGClQQsKkbWFzFiTly6h4QW0Zf4se4yQQJymdfyAR0bzgkH9SM2OttGiNveotV+KcvanThl36/sNBm0MFSp0MDQvO/Gc5YMaZwvW6hwe32v8+c0WAMXrj/wnN2g8CrPPJ6P/GxN8rCIIov5TuTMD4yLeUb8t7BhEBPmL0YNWG41VQMGgsJTwzN50QNz4zkavfikQ37ngPt1OUH+TRVdXKrvwMQvD7O8u91uvCnU8YQzKQkQwqi+JFYJEFow5oVZlrPcJ76d16M5StLuKjCkZ8/EvN5+FcKgLuRK1m6klX1qKxLG1EjL3ORFbGzsECoHqwfzVsV+lWwQTVrvWmEZ5/CPiR3wPvPT4iNIiHwAoPCBip+AWBOmBYbyeMiMvC4GcJwgLAcU7cqag/JlnvnO45qXKtgptzYfilGUBLIdLzaYlsmwbXiC/8P7wof5rwwJnFaqnbcP/HpV7Q9sHMR8dGzw8r+n0hzK3v3wCvF5NO9eP5T8sdAz9ABO7TR1wTxBVvMqr6a64ylrs6KMwENKkMLQhpjJsh8F+qqX14ORJdUPE9R6w7NZKpp4VJnY3j+S/aI0wHl7c1dT20mrrKMl/QoqirKSponRv/aBnLPmm5qb8pRdC62wkDLsyz1EqJzML/Dloaqa1balI5Fkr1utkcqIyewK0xB6OSKlNvfq32bp9HjQSn362o0V1GZL8B8RMxy41bDpbNcARWGc6SrX/ODgEuy27wGnmTlLroJvqZaRm91CFvqY6/XcuJ/nCafDvE0dpNEoegEJXwJOj0e6DcqLyiH7nY21qElokBOTaQ9WvAonGKMry6mASll3//HegHO393Kpvhsh2oKXXOQ0omF3lm67pzB/P3qI6xsNQp4a0MBTyfda6yhHjxTzWYawqIJGK4xUCYAIS7sXrDChUmu7DDFWadWg1s9n8Y4fVzwGCpbXSpJ0j+BRrEVg3t+ZF/IZeJK31zBRUIOfaSTV+GjPBQyCRGsppF0KO9u0456X37azvIuZZvhV3l4dTvAFXyzjTkodJ1OyNCbCtqMx/zBDbOXgihXPqwGp8i+NI8vf3w1L6Klkj1Vg2UfwLO3tI2rbfHJLzv0/VZOcmkEsWKwQm262yFcCaLwtNFdZ/HJEiiHO+Sgy0KIh0PY322Ne3T0uuH653Z2RsvQSO9L8c9GMxkqpRc/FDKHoWLGiXCMOfteTuZjdKtB27Hu699ZjRfbBj5J/2VvkncJGSw7Upwa3pMxxN6UtW+LuR/DR/2079faUazpx0JFy0jsoZj54kKPiJHIuqXIK9u0wkqL1JvCaWkbINtZCOtreFxxf5t6yKEn7BftqTNpYXud64lf62b8nOwLjAOknqtgY8HQsmZQa0lZh1FVL0vHJgJxCmB48vbgxXZreEA1hrf2VNPAPTrQ82v+UDXTEKTC6uR4f7mQulhGknKH2r2q/h7jOUQ2QJgnqOfPmu69q9lsMpjcYQPQGVPcQAqThZt6K7tYKucLqQQKBMNwBgOHDDwPqmCKlOodEP0WYHZsjm1bdFEAWaMlRbQnI5iAZynXHmaGWX1I6E+f4hzkSIEQlLELp23qKl7cDA7Z507MSteCir9Q6IH8B/u4zG9MvRtAZS3qfiEhWKpLAl3fllEvsmDfDkHfkiL9fRQCCfMmKEzJCG82kDbr1BJ8BMJgga3yxMGNwz9u2HSyyV3elyUasblSMdQofiGWZJO8tI3RvfYjBMMTvzoiUBR1KgjjYD+arhjIw46NyYg9S6A2X+Bmg51AiHjhZFl21Fcz7LZLMp+1YToZoWT8CcfdmN15uIqg9zU3g2NWrnXViM92smDtsCiNKle8teB8pKXLn92tTkDza6I/+C27/GL9EtWnDykXt8XtHhGQO6y5fVWDo51hXM9UIuS+wx7NS6plD3YRvrRRq73wyBdtvdNWZzHPO9q1yW6HEsc+wBOLldwFJq9r0vOAoctztp8UiXB/cm30VEQk2iIjsngXJLbbDtKLM5dwwa6kWnY2NmdB0pBUlt8MyrkFiSUGnIelRFGxUTJkZ33AD+cmQyBxQxKg28RtFpssucCPe8xwB/iS6TtwFJZPl8gb0zpDJxbqkUETN5HaP0zRgJ9EkR70fhO61WcSKpqInCOFMRvq2g6Pmvimz9DoRvP9cMJT9gnKIAAmdOPiQLnMb03kc1ibjwHTcE8DkuuIWOUM8r2S2WLHfS9wbOMEs0Sr3/0X9jhEOV3yTK0wOMjUQdp5WF9hoLNqIaIfORA4zeOegFcv4BM007QZ1mmK89N63954c4JqfII5MXrgA+nd/uVx8/ySY4f891PlspSy7yr4A/uyfrD3zzzCjD8OHJEuICUGEu5QBuJQtQ+l+/LFGJieyIR6ED+DZkT23fT8aI/unkntg+u5dP8WV9rEl4dOtuVTZR12ub33RsBm/219PO5NC4kfamFT7w5f8+ibGiqgi7p0alZD453sWM2ErkFB5Ze4OS24RghTQ3NsEeA3Vse6V2sipfdEhH1+PrnE7lM6AkRsutBH60EQ+EUdii8+GmpCLFmg8eJYxH0ugDz8oNcgMqsrwhWEYMx0iUtuCWdJfclK5Sh9RXWIic6rSY2kwFzFWOxvltONdz7Pd7wh5vNx93sO1dj/SISsqk0+yYDvIf++/N9PxLZYeghS1+Yyxh+8ePG1Xw6XBnV2T3uqMGfVHJQzzTPZYGZy0H412A2RtvBLV7K8LHNB8XA7Pz4l1Dgk2dCBFlNgWdkx1wxnpaJ7oKgqdqDx6tBCEwmN35mH8lP3GoQWbibNToFLhX94GR0F+u3mpPs8yZejLckfbttrpka4a20xejYHkDvH2tsTrTPixhO4lYokvVf3C6njbq13nC3VKMa3eDdLZr3JK5VGkNkvvjdzBokrLXsoATQDfYNNvbEuKsdkrn8Z67/fyRqy++5TLhd0R0k7Kcs9UCgVCeSJ7HANBgAbe+iXZ/iJPFsXjzR0BkJOlh044qC9FLtmdJmGBoFgM4vvdQkw7fCSZzYwGCV6JjfsSuMvYjsOEzZ6D8aiHs8dNoY4UCt3f0lBrnFyu2fKAeVsN0j089D84xseWvrehusj9D6EpbEXOGHBY4fkaEtyp3GwEByV09AsKjTwwqwCPE2X37t6aTIq8xIaWG47Uq2K7ClIQISfMiQGx8GzAVINzcZTWkGYEfMlVPqUNBXlxCWnZlOMl7JwVZ25woJc+AKXQnbVhY8+HopdZKSP2DUDctVwlYLbSIDpQU6RZ2VeTO0vigkJWcHJt7eaD++3CHi3tyDfdMGBMxSa6nlNe/obJbT/wl/HzASsHVVxRVRNhh5NwkKJ1EefnhA2en6UPZJn1i6SYEOz647j8P/VhJbk+Z8ftQi0/1hGXnhXxZc8yfnVCysawL3l/Hm7sycFGalT19cxBvWHhuIDW+rjjyzxWWPn6P2HCvCDTyZuAUyTMaAYDOE6WbKqSpEPSg8ppgj2OtLM0N/hF4VtfMB6bCbU6R6o+2IVVItRaR6drZ8PbSiQmby1bvgpRdbiv9r8aWu3D2n41nKpj+5gaFklnAN+NOIgFIRDWTrjLEWK1+Ys9GDs9kKdPXEUJ3ME0+CAsU9YjYTizE95cNVQDncPsJh/xhg2mSLcyM1fDBlQhp8kmF0qiudVFAg8HHFEFOEDuC4i+AHTBG1297LEXZQmX2Gur2kjy7m1Rmgp0gKszs8//lTiB/A8SvtFFdphiOWEaTRHndhbGtu6GqtoTMp7vUN6KrdiUDVrNm19PGSiTu+s/E1ankxpU54zclwx5Fuo2OLxH9JDRz3qcUEWz8hdR+Rz5WHz4y+whS6FmHBLScUgFum0KJ4lpDBfsx/DGfoTqMQQDM/53OCu9fQHqUUi7zw6nTGWBJv4lR6/pVSHltS5vNEH82m78y1A/Zdj3W2HG1Afm3Jwuy3b8wXxMWTRhh6YEZSH0njXmjIjK34UOr72Zj7BEdBpqeV0IU2fIR7TOVYR0KeIGivUQFB9d/KYBcnInzp0ZYg6UwR+lY6Ji8Fxs0eE7yVWChtebpEv3CiTKn6PyN86kxI1lNYP0F97fTu7B9g4JmDDrKXJXdAGMEFa9IeTxznSiQO2KzYmppAGlS65hVMZnqb7Q8Lrio+sGmjgEiczvg5+uWtuC8jupxZ9CVwr7USm4tLx7DoKbt+563JNkqOcb/rzeKAniqjvfFY9doB4IGqI1Id3m8ycqbTXdHXDp1DazxqFMp4LLZys23JcrEng8nDn4Hhg0TShRTzN36OIhPdXchBeQD7asHBkNySehuXdT9WWjS3XozpSGnbm70FHpReynV3y2m+E8Tx3CgP3dELP1IpIjgpU/N4J65ZozKxoJBeM0hRMy5nchuzbLw6BzEtHUlaEOPY+rNweINc+V5g6+LFTErZWXXn3rR35pr7ar1cYL86xp+IDzuWEKyA9y57tghSU44LBewpclIE2nROU6l3OznIJKKC4/K9lVEvam8qsO6jjpUkFa/qjThzyHxDR7Wz9a7y/MHLMf33EMx4n/b/Ny42v0GRFUvG0rIUQLZ+nWwMh+zqBmoIBA3tSRJEMR7mJ/k/oLOo20CvJwRaMzghAjk7nyGlZOC5Hy8Dy04jWcA/C73bFQBVwR1lPgvYYXMpd/SWA/YxJzgjgcWT2nqZJg8r1x+TMB2JjuobvImVjUCzaMmGMyRzMKRvq856XzydEtAexZzOYse6r4weVfHuG87dvqidoF3QFt/P6KpaxQ/EkZnA8FDwjg8YrMy4axlN3TTUI5JIrDqij6R4cpIGzPSik/G9QvtUleC6l0r8xexhTO4QKCX1NMnnQVuOlOlf8nK2aDXTci8c56Q+NgNJO2cYK52WU1xW401iNmXOZX9ZNPtkrUIwXXyOD/Ms8TF4Oy5IyHI1e3jZ430RQWHcZAkroRzAGngeVtTfdhaiPyyvatNGrnGcvt51ctSqLxtzR1P3YSM5hXDeZCwvLfsbYBz0ooOx2EexVkn9P2YOXMQV5ie82mqEPjj39++EYhfnulH8yOQ9+WT8HzvhPG66Lk2znAZkPAKWG7i49kIyIAO0D0vCR0BQvegXwWw7gDt2yKnb9YMd8/HU78rKPkX37U7jWmL/kP1NoYx761GO1iSBYgN6P2yWOh8rIZQyYCgyuHZG7X1mpVuNEVfJLvhyOcO3/nmOP2ckKR7AbobA4FGdmcFkXHJOLFRTvagIESdYdtybJxPqdbshpqCicF2Fz/6jWTNP/G/pXoc4iNkzIOb82iznM/ZrF30Rhzed6qiYZozeIsiRS9/VxYF7iobX9L/N5Xa7p3YeWfTN9djUcGFCLNpqFGoctc8txuM+mGRDedy57kDiQRb29huICuPuhzhexiqq5vfZoslNRJzRF+ZrHSA1NACYLiWj9oYlDJmdyOU36mq9cUchDjWcgstnO32RwdYK8FCncqvARnpLinqIGGyKOxrFXM4VjkdK0O9Y3E3drRvjuoQjFjtXmEmQVu21t/BGdBaSDhaisYxswug3Ncg/O4d5guw19FPvKPkoHQyJlLtBJQhMblWcCVAvJjywo7Nxdv3CFRT52qJ8yOjJgppMatIwbgd3igAuBcZWrfRDUqyG9rRTaiAi89Z700bfRLT2pydmU+Rukr3qpyfmGqufRPtWGzTIv3qHn/j0lgQgUTX7K1TpL/bdCf5kQY6Eu3a28uUS8XRMyD8NmILwCqn5dJXjT7lIaftjTnR2FsKYJdln1lDL8KkZZNAJPArrw9tL1qoABK/dYw2O/GTe70Sc7lZ4noZvd2mfRhQMBwUiuF/HffDSFIxtHOe9/IIhhCfQL/sdF8bHphdf+mjhVdJkFOezSDlt40GHwevXkEQv3FK6txMgsDLV17/lPh9yccrLA9FTnDYnjEFwZJnc7JrVhekdU6CqLUyXBbiCh7gomJZn1hcTdFmy3J+JNmqyDWxcwyMadvVUaCcW7BJU4VtGDsWi0QDb/dWc5hNqFe93de6jhFwAHFz1ewtIkDcZNcjEEu2xBpvnys2lOPuRp22xqKaFOEcfCkIurw62X+/jiYJ6B/e3YjNVq2KMatU2VE42bI9dnWTFS/oDL6pkZqSN/NcH5esVMkMjzsE98FbPk5HH3R5eZbdh2xzxo4NF9qQMy5bsfj3F912kBVD2FwR3S9RPpixM8VHygJEYTuWoY7IhMkAD1vanMH0HGqhtkI/IneDQdXEzh7UE3pG71/Vo9pf1e4cgva/HdnevwnkN9iZJHwcaHCwsIm7eUTZC5gXR76fky762SWJrn/JsOmduLE2rSXQE7mnFEHzAg9LsPqYXlqGBQDMSyph1s6lhcbJtdKbucsHskif/4L4edaUkA+apoIjR0kEUuGJOOe+QDyIy2f71quIIKy9By4GktHIagcMn6GHX574/g2fqxMw76O7q88ZfDZbSN8/B60UjRb3cCari2fl+0/mFltVuB8hvWDcBoCsl55TgjfXgnG830h45pY2sVwcVPbfGq/8Y4ZPdHsUyHFqi1N5o7qNdcl5eUl5wXPFsqiYlfZbAAnL0FIUx4+E+uDBIofyGtdgWymk24r9zb7BnwUY3GuuA3jcrcNFPQtvSSmts75x9yDZecOFrTiWaHQrDw2fnQ8SrgLpD8+lnBEKmX82+osYAtaeXzJMrnwjd1LqUiNL+icPXKpjyWHN7zPYvW1YtobdR9REaEKckDTY9qurGiVE3b5CRQ9OrDrPLf/rlfCAgXpH1MxdSxmCsikJtv2jhSCrnTJbIKrt+GGDnYBqqthQf7X/trzamBBcDAJ5IAfwrlasoSPyKx1v6aO8uA0X/bitknXRPUTNP4xtQ34jFUJ7BDS4/i5hDLfygz9eAURyfqTMDKZSZr32g1PBfHWuv4sx1tGKuNqRpqMStNFbfSFZ2v/Y5HyVmyNTBlCh9STNGbDohxw/YF/6Hn5IGR4aPFIBoIHuuJI+WNVAhT75dWi6ojQYv7cGRB/ssPxkfjJ8JrTD3OGD08+ctXCU6524NFPVNUG7BWKUUnlOsP4mLawpIrfnF1ai3yp6Sybin4ynMF4jxd0+n+wUHCaA0p+rJhdjv0yfRCV5ry3fqBg2rkD2wGyh4Ge911UxarLTKnD5EagUYGNZ1X61vJAzBU0JbbxdEwtg1lr4qM+4L7KiWm76FzoEclrk7ZnTVEujJLXQDTLqnUIKNDm4Y4LOC9IEYXBBJ2q60ewCIdtDUHxJMAj053Ldrcf0DugFyk1C9ysTD9+JF1aLGmrU2hydlEzW/AT5nHs2zUU43+OcvVUtA2e0wACR0RpuL6z68xSCEWfjYJdF+YIw8R4Z+KkWat+HXj8yynza0wmJBIccFqhI9/uWzOzX+OQczMk8WU+UyutdE8/ISXYC1x2ZYcE0OXGy7eKMl5CYWFV5puI0P6uELgG6mbyzF2u8P6LCuEnsPCjKaHBtJQrnr1zYEQkZig8Ot6aiO75dea+tqCOj/VhVosW1V5irTznvrpZUEwD8/dXEd9m9xOursgOQuNKgHsaCi7uwlSXoPbJ97nyZBQPQuc8AZhFhjuv/CURh5XXe7IMyn+wgxxIOgUiQzeYJ+xISX0fbd+e3KJ659BC8V+gdy11b1F8RzfE38g2T/Sn7gRmu1oUAzAfO228aWEwIT3OrB7KMHjJP6xhs6AslTq6NUwc11mAE3DOFbgnry1xmg94qpDWnNnjQab7nECFiRnRpykpbAgLzC20dsHrtt2/Hgb0qOqRVT/y1sSzI5WchaXGGD1o5MKI6aEFdcG4/tq9AW0Y29ffb0WlylviPY30063R1x8bLF6A/Ac9Ve0mXTmIWAs4ZRNd1mRlBCQe9MgncTI8Ly7uvx3BJeVZlyzFHpXQw4PX2SzzJkX7lSJ2s9osvTDAF+jxFgEPRD0wmYWSZVlwl7MM5X2xkLF0X/xelH5y+3VrWKs7H23XsGBjwjd7PABO5CZTa7MuzVKOZA+wz+fZOzi0y0KufK5cr8vVcbKBULtZwn3kvDUFM8GycQp8/R7nAyYQ+S1BUyr5lIKC5ySCuMr/fay+VKm9OdG3Sz/jrmIf8f765YZTEhrd/G5Q3JnmSXV0t0RgLmb5iF1aw/Kvqb784i77+P15jn/2pj98wW4C3C3jLh3ZF6Pph9k8YAkSfBxOjPAL+lklvNH0DTuxPH3vc5VxTSvcLU8IAYNqDyRZE29np8kT2pm+yg5/aeo2t0g/eDBz2mJtRW8a7P4U2Pb5+eIDeeCuhh0MM1J0r6yXiGW+usUcKpEqWPiWhG1Wv5YIPeD4heoCj4W9OPb0IOupZJt0yzPrS2U/xtBzk/nmrT9iMila2yVWFro6+/4ZVjQFEnxjRwDZNcupJ9B6I87FPmq/v8MqqbNvH7SHyNPLQk6ijLuqvURqx9GcwD8yKYftXIXqN8jjM5H+ymsx1FzdUPpVdVx6NJSbLPoZcd3pfDelpsxqPtrrH6CCtI1td1aaqdoomCq9l1TSFHexIID+56oiyC+5tuR4DGPacydaUn5pWeM2KZcXhnkigQdnJbo8ayi4rw7cSOwQ5NAvOR4pH8Wdm9C5u8R8i1Dcg9ih1pvLhq70ooPqj2RycOlYBUdPNWyHmr62GkEqxeIvWS0MUCc2yp+TSq37Py2O+7vAbSHVJ964Hls0ZgpjKYVz3VIzqICOmwraPSetm5u5HG4Hudg0NWO93j7Sff8I9oU9Gh7FASYmNCCrN09oNy3YaQ4TFSA3JEeW5/XDvA3i54HdmtI4GxXA21QQsgWwPKvp3GP+EDO0f1yASUtix+TYGjLOD+Jj8kuVD1+qRaNqvSON/8qSRZZd4e4eiDwd4RcAIgrmqjqcnjJl6ikR2IuxhBHb36enbIoBB3ismPFf10tyPScZKd+xkqmT3IFR3Q8Nleft1LSS+61pTPVKN2MgkCe1cHvLjlGDgORUVsv/Dw0FYZ0QFN9CTs9HFn9bZ8YkBIPqf6DdkGTVvgZzelLQQpeETq7NhF2tlzn1tDmTLPDOBk/7k3xJSUARXBfmyxTk4P7HxT/dcy1Y2lT0mcwp2w2PHz7NoKuRWIYRKHqwXLtmNNxvx9b25CGqUQ9Ae+wQJx6xoQYFgIjfGkpHBsrEmYu/Xvekq0J62ojhIqp3waG7Q3Hq2+sX5eZ0goc8/6gZcbvjVgDXqzvE+THIAM4HiEHoXEtENl82/Bk/f2Dssnfmcv+mdZ4iXcUoqNk7ogp7EclJvFyEcqtG5Dsc5tcg8dAwdp2IT64LQw8zsSDf8aYnKGMl6AikFup9c4W8G4kJ+4UjU3fOpBuc4JlOc90VHTRJVhsHjFCg1XOSNNs+DWzdKfQtV/AelyK9kGXx93WodW2BHb2jX8rvOO7yQDVvcq11Z1PXkkLw4RiIB4WHZzGxRTBWxOE6SqYOYW14wcp5jjfXz32Cg1Fk1V6DVbsjAYAtHmlUtswMG2dco8x3/2ILGth9zrYI4I5YRtG/qIhG7n2LaKe1avbybdaY/23sHpTqTLYnH8LtS/91jlBgP4Z4u1LUd+VRnNh/3v1RuEJvr/SeySDm5I35f76qw64Svb/u6Zv9SMUz3IWvlu0H0P8SL3XC5KGq26OlFBdzuG1XUvdS/8G9d7rWp05f7ho7icYGpx9iVZIIWm9ryBr0oPUfJ46fK6JBGzPiYR6FJa9UQ3Rrv1Hjw9pHqAeyYaiv02dBHI3zwBszfWiM0RzD4i5fN05HkHHTY8hRQEfF80jQISLmzgMKA/EgQY4kmRZffUWUlsP0ZXD15ZNEQjtABkxBV5VzZpPYu7kK4D7meAXyhsRfJL+OAJ6aaDQiWHlYdyzuUePd7bIpL8wfXhwFfgXilSMCPC7bnHAfOVMEWAhivTbN32UFidkZ+2gwQduLGx0YcRO5JNXxE518POe4n4L5EtuwVnOE/LQpsA0Zw42ivNSjAt3/D3QIMRXc4UK7TDdseg8oX7igGcLkW12JMDudYTWCt4UDUtUJWqO2YnTxmZNpO96wzIIYx9cvDrjKkqU9TMb24fOeHWsk/kYamQZwPIRRtdFczgjdhUj3U/r2QAjUlB1DinTKTjqncgerYQbA6qTMNgD/vTe9plCSorm4l7Tj5s2/PqTXbks/0rlH38KweeMaadnlIxdOz6w4JYAbNmEU6vleYgzGFOI3wUJA9ebWQAYksNh3l4V2LY13qItW1jAQGK4yoRZOV/tus/bSWntJ+8+GNq+lxcYLpJlwsr60g8+3eqOiUU5K+jcgS/FZtcOA/Sx9mO+cMQjJqaBrf5HTNeewk5q7yJLU3fH5s/bnaCtBaeZoRld8QTaEw6FBYllelH0jfXPObwXmVIq2+d8f6gViZ2JIVctHOSanwjxo9a5OPCoqrP40T9gERIBqziTO4hZuBJI4UZ4uxn7Q6OGZTm5NmUqmc3SlJWW3NVZXa7rDl/x++lQ+oXvCxLN6Uj3hvmUxlBxG/6NMFChiSihS4jkAZ13BIxkCwmcgTKQNYu7qt7JeZPXUtjsYpkSAC9EWzlVI/kNSkNJVmLrBwjs6Rscrjt4MNUlUoWGpRj0RKzDdQRMjrKWQ6D7CNKjZVMEv2JbnySsMWYP72q0nSY0YUNKU/q2FS3vCYN00wKsnSx7y74fXnCoZe6vzLyyG2/QRjAv/1u1n/ppHU/AIhJz4/ZvRBtR03yHH4digOMcr25O32L5mGaAyfb6VDdr8eSRw2hJLMqIPG8l+d/Si8J9eHDGAXftfbWoWaJxJ9wDEDQgeAlRvewK5yzi/1lXu4tX/02oMd8CTgFjnoJnB0Yq87HE6NLLWk/cjAs33PQPMudfJ7A2sfcgeVVK/4dHcd3hGP2ROUdiF5LfaBqDxDbJj1ledaowW7jCJRyd1R5xBMPHKEmdM9he60nb5p+dstNqOgCM6UXsxZM6yYw97ZJrSLJRwpdmOKoJ3IrksmVzKTxutdUackfRdcxFvl8U1E5KSSGpszHGFv504ZwN6yNA9IfkF3N3zrtET/Gc1ahdsBW1rGzwaE5pPzXUrXu6w4nvnCADScYDMGieujkpAmkTVhYZP4nGgLJrZ8e8iN30455LpgTYVLLffIgJHqhfyooYa1oC6fz3aB7/tgkcH7E3iyR0OenKw8yvHWugAP2evwbb9bpoa3N1XvPopcUyzhdghUvZRLtEdTpnPL6zYChl61oHDSVqJOpre6gcCdKMqEBcE/qI9N0nrYc91+kL2Y0VVfvZc/lqNOsh41SgPsJ8L0pfYG6nunShs/1b8OBnYdVwX6yE/4W+QGIuT+2mTuqnHGpsvV7V+uw9SDbSV/3W1BpVpjk3NzVSa5ggwi+Bl2ApVrvLl0iGM1ECVjkIX/RjDkFKQwOdzoFK3OUACLMTfzGBwNQB2ozoBTD+dK/SEGg1a3d1zgl/BdK50iFrBKvbKd9hmeVkbcKGPOfyG0hP9t9KhB8904w7kFLtqT3XiVwJwz10le6dTcnlxX6My+gkl1Vv/3olKORfxBb0oB5C0gOCvPohPMEvMSNknvTNKBRdmKCpgQ8oM0iOkFeOxKtfdlZVXPJW5JU5D4UaDmPSMzmqYFhZ/SJFrg5GpIbNRHFI/YozaT54qzFSzwD/A6+2bIPY2v3HAasz/RUqJ4CWouhFtqY9RBMkosQX7NFVG+rYzqdWhxh6/g1PgN2njn0UbmHDmBQiqidakQT/fKKIkuYhqw9f48lrKfbqqixmBOu5uFK8ZJE8tqGqmIsc4UI22h1p+pAisP0Bep5dka9HE9bX0X7A1WVer5dBb+mFz1bLXH1rR+2W7t0/r0KeBQ1XRZEO/c7jvsRP63NJBbRg8IKQI5AxdUVlslnMbzCfIGaooWRaNyh16Wsc4QRPWzXfiKbl4Y+lkWLsz1TbI+flLy6lB3iFZA4NMTTzkhY1DamInbcqz08OkyEeLASwc3UyafTudnBGmnBXutqylDYAapDO0myioDkjjHYeQ5sJx37HTfy2RT7ddwSer8vmMzk59s2o4Rtb68bAz93iuzh66SBF/9kCs1rjP9gxHxGW5cfXSaZCTMCERdHtiOTKb2EC0fPROEMjp0qvU0yPb2MOSzGW6FF4wAvZk74vrvukBb+v+Megidk0LSIndzocANPf+ADrHPB0Z4K6/9rCMc3tyNBEKuHI7Y5CYbH91WOY8/tJWTYdm4p3I8ISliDKOSvUN6YmPR0DEegwcggD1OCMSYdMwHzeY0cjglwYKyZdBlb7XDILilyB1HyDQ5JiZRs2Dh5FKBMzRQlQVBM/MFwgkbaS/82hgEfJyhEGFRmGgY0+9FdJYlC4ITM7GUAdkYxRR62Sw1aGYUDDWPvTFgUERbMygDUXcG3hAH+ro+hwJKE0Mre7cZ0FWqEztWjrtQFdioiLxdQDdBSzf+6utsFCsbxLKll9LR60z7cxrkA2CRPVzzmLZU3APCLO0mmucUXd1mwc2EDE1JvAHs6n7x5HTK0gbKyJs4jCRBj9wP7weeiii2G3zfR1XWX/RF2juekt3In6SOkUoX3sZCnMQyjLteUfqsHSq0mT1HSA35EmGQYv7Dxu7BhgSFN6JGAtncjd1c63zmfps9ZCEeSUWVLrSSzlyjrSPtop5B0ZnB1tXJr+lJNbE8tWM6pvnjwkO06710goU2T2a9JDOxdVd/mmEiRf1S6tF9bRPwS5kKBCwP9M6jfy3xUpNmzIE6bFZ4qB6a1ZADYw5YNz9s9r0OndA+f5BZUlMmWjNqqmduJO7zgZ+Ssd3YmoIdnoMHlClqA4827xPlCWMBNGV0t/V/7dBvQX4u1Gnva0G8AzWRWkHv1ZtNTT23kd5+38F6ejfYkZLYK+ZDVKs1bgI+saYxD+FmkEyecwrho7cdzwUfv/K9K9H+KrQQ8251bs2fBj70HbNXMRzpfAgCVGTEMYgaz4vMFCFtrXuf/wW1KneIIi2X5/4Xwkm0L3q5bRrjbsmPcCjLvvaWEo9jBu9X56CFNM8INcWcFMs3zqr02cnqRdWivibb8eSzOzxNlJbUy6XrAG2TC92/n1Po7lYaAEKNovoym/hPtoXUUsFPJ9SsQs8L7T2dxIAt3cUnIp3UH5K6yJrpie9CtNNPWaG0j5A8ZUmJdLJll0wzWl/qcIl2dGeeY4ymLHt47QNwWRX1V6i2T9NERPerHX98NqC2EFhbuTAbOZXMlVd2IoYvtSDQivL+9e60TFqhyVDAFIC2d2rjrVY5dAQPpdBOfgejtu7AhgS9iqKvXZpgzKdrSgEaxI96msCIS1u1u2mgF3zuT9toAAh173x5sL3isJ1N/tTfg26yoDB7jx8FGLcVa2VTn6qAElbydgfAQtQtV+hZJOPgWBSAXUWRiUzHM4qGNXg6DmbTsKXdBbXOdo/6NCV0K2LXxarCCMH0WuzpIRElB0ctcU1mGGoF3N0Ef90kTqySc756iw1gcUk8W3nljADS6GrDRGzmtb1qAR5oJlxv3anJZ2pqfARIng7+UuxXKIqtPBfA/vUnAwUwZLoO3cLOSK2wrj+Zp0wAYnqwb2qAX3lyKKXsAZNheoF371IyDHbHz5VL8yPMvBwhODW5gUw0u/7VEPBu6QJlUSKsfXXuvO6IIcZ/xNXhtWRA1CM2RZE2edOXjOnp7ivDcBzRRh/QI0nknVlfBsLZEROH7IUkupYlp33t1EITNQT2Zpz36BQu1/CiCVC1olxi5ts3vliWYN+7OZFR/+xqCZ4xe10woZpBtHa6B9IXhjHT9pEWU84RYXfgSa6iHu6P16z8mwo7Ont2YxJ1CGmzgnu05lGHV+NMR9YU0uTEO8EUZu0B3GbOBVQay8hj+o2RTEOvhxEE9zqKYFrTLZIqdWwne3nEXYYuR4FGBWdDG04CgZ5vPzPhMXFQmOPDohkF1ny9KXBD61VpISWniI5ooO6MsRGBpJUmQbP9fl2pI5V0Mr1oEP6YavMdOLTXtWtj+EJOUkYrijTtW+Dgx+QPKp5Y7Z7lAbIrHe/SZyfe+VwN7X1VnvKCbf+mYskH9WtikPACr3W3kAMVZbEhxQYbCIcFi1G3ryQBdFbFZTD0PLqSosqR2zz9J+LvKJWnGq6nSPZ+yk795eJAz3WD2hdELDplgd3p9qcQAQ3oXWZR+GyS9FP3zpM/l83kcEDvXK3E0NgcfD5dbViTsFQxToUhr1psHQjGdFd2CeUqWFubwRzpOS3jg8MKnu1l2INGIzSvv4x9dodjQR1v9SikTiutBMm10a82eeDBU8ML0xwPxgePCwuYGpkGiHv92wcer2ER2l82EAdVhRLftalMXRpvoHD8wPYcwt3Swk8U+CgGAPJISodqcQUlvCMMeE5GVSFGs21rh7Q/k8AeqQuKGvDdOixfT9CmhWh/gv4f9dsT3jnX0DjKeRL6Mo10ndmzjgjA85kl5zY2Al8mn8TC2vQsJFlIPpMiooaDT3MLLCXMMKZIBkn8qX0mZw2MgEj0noM45c/4w4pkwpfzyLc2Iu8T2CsHD9nSK0+3Pk6ei4BoJEbh2c7TdjQqcCF3TOiE5sUDeEOmOK4ArQx2QOQ1mhVP+XXHQ4/4DgHz8QFMK0kpsba2o5Zob9AobYJhnwjGRXM+vpR0+aA1vvzzqUvdVoM+uiR5H8/nTV7Kh1voPmsRlT8SolOIQZ/p3zLO8PXnrmhwMHJP4TM5Ad2S8BlYq50/4fdijRBRby11w3k8r7LbM8TguSMYpuTsqs8448YVT5uFZ0EyFdBO/NX5R1FzEBS13uuyaKqkMHbuyu+umE5Yo5OWIemUQ7MjI+oZBEai1HGNHorAobDgJyDK0ByCR7M0DHaDKdFGYT3qf0P+6rtP0lv2NEefX9S63dOTNDvBJXzR9hvUGs0GpohCzec+W+YQ85gYs+VMf7sYcssyO1ss8WO27jFTK+etJmGa6UmGxTpjpka7YWTo0CvF3JkKqLutjchxMD+XuShjq9IsHAgaguyBRnVUQ1D2fMDJrP5kQKY5Jm/opFS0E1KbAaLv0dm0GwsxVrLblZM/YL8W/I/AIrIdJ/yEwr1rfO1WZNwaNG9X9/40bOfJG/euUA8jY9kuODxsvq3TgLoGyimRaqMpzbYoP/wCjU1FJ4wkJoGgLfhloYXJ1mtnclMrP1kxxMAVtj5NT190HLlt7IHWlQ1jiDr2Poj7XuzVgzt9BKs665MewJAmEpJbKsJcUPZ/XOPGCNc8LiaUnvZg9yAf9jLRUXZ7nmQjb+sSYY4RiygCKL2mMY7mBnJzlQG+PcW21GmkUrW4RQpm1c7I45cot/vXwnsHKL0isPfKoDb1dOEMi6XfU6GkQHi/Qsyz2179f22rH0gAS3t8ePJIEXkxXzCmJotdlpbZ9j5WnxkUXTq78cKL/JBsQkQ77CAbe+X9RiMIb9a2ieleA00Hgkj0Ep6NCiSO54hPafQeZMZIOhqgjhFKGGfsWURREudzXQJ2gx0H7o7tmPSaonXogTnu2sUTrH9/W4xIMvmP3o9BOoKGDJWFjXO5u6gyWmqqNf+SoEFGr9f1G7qYP/X0aSBL6PqoS3UirOeWvOxL+3qMAvAPmG9hUJ95KLNo9PQs4R9usOSSPyYGJ1+fS3WuBXXy7oD9wlf80GYXL/wiVuU+6VizxMvIiqRLC/K+GW+Tm5++KjhzhIs87EjsUUCYHvoBwmR/Ss0fVWq2fD4xu0mgWqqJHofiY4LiJcXWwK13yVgaXetCA+pUZ4JMP3HolrHSuaL4CtjoJoSaYeM8iAp3+CzXjBtR/V1GuJYHgLONEehXAo0L0Z7ipFv0cQbGB5ss8HbLlaampla0EOd0PVirgF5KFO19SOb3LQPj0LVJPVspkWGYvy0V3Hn6Uo1EF8qdH6yktcNhXLF7ehv0Td+Pjy6uv9A79XK/OoEI5lvDXWX3JWgfEBeXufeZc30Lh+Mz794HJD5Z8nlaMRWPSofNMzOTN1q7pBk/+0iHyi5dwO/t1dtVRlW/FpAkfOIiRmVRefrqraRaD8bqBeFNRmA3G9WwLwJir5pkIrcSnQcKbqqnx3gC+Z3rjJCW4brbBeyiqREhohWznSSNWWAcTH8nQEJB/6+XU0f9mHG/Wir4tDXDMOSf2hcWBzz8F286Jy+VFuaqSQ8jpGk61Tv3YpwjyhLIv07YIwol+LYnIt0cQHH0ySeU/ymi3tZGI3394S2+LioZtoAqf+ZvOfFogrfUA25e+Dmxz6PPEtIvpNpmP5Rbh6KGooommdujSBeve6Z+Bk+0l3t9TxXP0ZhcsE7ov7XzktJ1VZnc+HGQeDLp/XtuOV6VFL/0ZVmz+bnOm3MzcEXRtVy3ywg+TOsOdzJNwXIP34t2Rz8mwNQgvfH8L6l577TEbiaqYVMcr5jCvkgDiF/JUxXqFRFyfWlxVOKd8AR1PzZXLh9GSYbv85uxAjSSIuBp5H91dbBdE8c+BHchfZhJDSIxBX/Izx9P5vmxABTH85F6Trx9MlKUlT9JYczm0kZGY7l3eI99njj4ePOnkfuKumHU64rSe38nQ6awbKOUPIRyBo9H7eOgZ7h3lqbHEdMaO0OmDovXgw+4y00/NyNAM8j4nScWztlGP4XySzuV7d8BmiUg8eC5syRR+xA9O3hILausetAebfAgg8b1Z5r4Rl4Rmqv/BEje486MkXFoCMzj3Atkl4Bnvnl+fE0Y3ggk2HTd3a/J4VC9LWH1Kyi5NcH4suJeojPa5sCvmZNjNeWudaUxG+Lnnc2N45msq4lVfkk2lWpurctyNLyaCLUrYiG80HmxBZaU1Ukq68ideUgdzqmQsR9uRfIWPSr2+zCQIgZppr8l5u0QyCLte0CwyxKvfAt8jLBmeEr7brNE8YDOTX0hCaGeyogOWEIbstMTcAIsPEbmTGCOT/GOphHTIy6ZMn5gESI8L8Zhz3eh5GNaQlNoMv10QLVK1zMmE9ujYoriuNA8YvVsKDynHxvZqQ+lJPAJsIVhTTrpCqKURzjlAVRYhNlrkeWo5WOX6ER12yXq5EslGSRHWIeOGfb2ZC9seY3r7RqENN3dECpDAMHF5qrSGzRpr6S8fjei2gRkp00sDbt+euGC6Lrh17LHd/O2owW/ffL03Lgo2Ys67iQOUHKh4eM6qp/A602COeFGluqN6u178MR0pS62bwtMfXSyAuD/LDx8kfcDqnUwGWrYidTH87DxnwNCgtVmvNpGNmw8wgLFEZP69iXa6wyAnuixC82VM3olJVPHyZrLzZSP3yuDHOcCk2z1t+0AvOzROA7uSf1Xfug8KTxEB04yMu6qJIC4cirPZ5nDjU0W/s3FPaA7Wp/EvCEV5k8Vs/s5GV3namw42LkpCfEU28osCuonZKGspdvOoKzQpFuIRN4NcWIiLvemOTyHfIFabTSJga/G/JTMaeRP6z/SOR8J0MpCM64NAVm+K2bTk65fJLh5aaX4dguKe1NKf3Ih3CHdzSMUJRq85rbC6tUYPfDM1PB+dz6vzdLIk8cfhuiPg2xFlRtAkeHZVl0vXdgrv40knPioQXz7iS6lDgii3X4Zm2sPNe+Y2825UGyK4BKKHm3YmuCEEoM0qcf2cJGkLcAoQD/uTqb9nbJNvLvg79ITMvH/GJBj6hxblA80u/c3Nk77rRsMpsZRaJ2LCmVcnMaLd/L9IgHuMDYjqNDzyxsHtvykilY7cwFz072U8PEWenM9+Zd5UPAjamZ3L1cmO6TzifG27/eGNhRaut+P5o+K6mrSmpxp6L6KlgU25/XQ5GiEqj3dzm2Ew+jIvKolJHSuLxoyTIA5EFbdpRp/PIyaqwyZzcmSzKBrrRUPbXKTS0h4rnKMc+q6AlQ/UL/RScYuyNJ8REkQyoRaJUcgKTF9A5GK/XSGWiF33axg2eFKC67sKKG3plsxX7P3gEAkXch9Gz1C1QiD40E0EhP/FPzeVYKbkFRxryuxccvVqdkH2OCqJBL9GLm0rkoaKOUmV9szZmuEP8zW57+gg0d4CUw8vRzj2a5tNzsembRSPALsKHgk2VxoQe6LOygctY6/10FF7V5Twiabfh9p4wnm5QJv11h3V3VpEPcR/4pNhRD31x3pUgQtKipCKisNUxj0m5MhMUvDyQaqDjpvUapIqhKnCC6uuONSneQa4KfN18cqRJkMT4uhDCK5lkXMpsekGArcEKaPfnT9qGiXKD6bn4M+pAIkXbdcXxFzcpGbBdvpIrY8+HlYBXjA9+3zxzmvgFxylRkFaChK+nLHPNwkRVSmWPNI/G3cXeualq+/EW4vLjiuD+JUycKw9HsNfDMQKP0c+72Ccy+EXdDsJXXwPeBmMWR5uvOcTQ1JVoIGrr7J37eJgaqJeq6htVqJawgQSzKQl0K7KrbfFdvJcPZwctunCcfKxIlKM/CeLf92StPBQ3n+0yi84u6qE7R885Fuh4FwhDoS5Rx2I69SY6cY5HDueEuWRkca1Ag5JQTYoFme2Xex3yr4jWhxLU2FvlxqaVMEiJy5iyPHj5ykrkNW8ueYjg7AWAEXbCjwN+7KDPQBwJLPYrbMBBwE1YTN1TiBlOcfLSbP/8dZtSvdqn0SlhY/bej5OTnEuZHCXdJkoWIbZkH/pkbp1RoCjY+3p37D6hihrRlq/Gsd0WBaL1Ei8jmgbQ4WJH8jCDh36XOUcPkXQdVWWIepRKF1DGy+BbQN36SHQQ+ARevnOvr3TwzoTqr8GUguyXoOxMmnRM4ZlFNAUNVzp9FW0lU51W2IkoGKrA5NaMbsUtL+2PnaJ30AnoWQq6KS4nPT6N73LNqZIYYOY9SG6+ZWdb1ymR+6sdu+Du6bVv8JOJMKZPyamMDYNJZMvZ7+ELxuFBrnWxnyAEJUt4+QknwAxBPIAFXxU/iLh6l+UzAJ1INfVdnw6WXwJAF7gffbCr6pfK7OJVt+jF7CciCRapPuJYMusD8Igx7styFCW/3sBgs/xHNtInv6vjgXwAdfn769/RMJAGGq+uy1HjUOEw4u8ycPuPbzBpmWDSYy++FMsQ62cbBTsaRAPaYpwQbUbWDY2LSJa0gyVz24AvzRWnfvd+fNF53LNemL7SxYz98nUuotD00sQYYZ8GL9f5YXiBg6ciwGaUPTqUg7YiH9HuSuWePigZ13d7GTzInLZImRWJE/JSnMn5VOhgoNURMpNW9FEbFZEqs989lXA3FR2KIB5pCB97b/1mw2r3tMOcSnCr7miFQDN/snUbF1pG50Cl2aLrXQXdcfQKtGh+eBA1AjYv8UlnbKPXCI1B4L9oJIAlhKRXmmxOj0SlKvYZ1QmEV5VTqpeUmfhnxK//AO3GiT5rb7+HaJQ1+dZqS66lSJfHJSnsqq+rWjJoPZwhHIG67Dro2enPlqZkFpX8Hpl/IIOjUQD8t1CeMt5BVMDW/mcFXvollPz60s+WPpfOEu4BVB0Ck+3+rfcizxrbUt3k/aN1eHpjRfN0jrXQqYGPIZfDmfYuGOHL074YJ++Pr7HheAJqzUUZdaxsoHLPNIfrUcKL9mvG8xyM+nvQq/ZOI3DX/WcvVwBUgBAHRFfVyYJh3FnDWdtMvurfiZva8RA1zxFOtvMZiPp5yHmpspSaFiWpLBF3KbgLeoMnGynZUwdMadrgJdHmz2aH1lMgL6sNzPC0xsQnUzjnp0FUcujI/nAi9ZHPdn4pAsacvsC9yinQN8lK9gWEEo94V5fwF/rMMrD3yrtoV6MxDcgrsJa6RSVmKm5nOhlULrnrlyYzS5XERRhOHO0POn7KYtWGLAigs8R5qp6p8/K9SqieK7alBCkSxD0ibF9h1yY6yEj3VHIdfBSBiyPzJm94clRgAN6IdzoeYeqwe/J4kssslBO+wUx1jH7mUjVQl0sMG2WiQOQnIF+pRT2N35hBkMb2Tu/FDG4wFaH/Zas2MXhS2Qinn4WAb1U+t9zUMMN6ma+LrwWO4VCpqowgKdKluVvBSJoTf1/si09oqHxYpixuZcwJ/AEUM+Se5OqymcjzuyhONBa8UOQnwfcaFXiaITdlhBYSQvfZI//zwBfWD+VC7sGlCvfOb7BgfYgjextpthBwVrKxf696SCFAxNu8HwS5tmdG5V08LyUrLO5nBn0VsA5QjVz4MEE=\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"\n","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"Why","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":":","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" frontend","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"-design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" first","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I���ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I���ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" gather","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" context","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" (","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" run","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" /","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"teach","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"-im","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"pe","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"cc","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"able","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" if","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"LElKq9fY8CyU9HssDOv33EsHiFJrUknrBQonL6f33JXXDPb1TUnwO+uhz+quQp+kdfc4tm4gQRMxLoG7k8tHFAtj/17/bnj45NO8JAGgtUneVlpSpCQopg4Awtn8FC5hP52gqkFuIHosXKicHP3eyGx8bX7IdRj37bQouPqOtPpORVozRhJD469V4b1/cTFpss5gEMB/zRShsBrcMS/ooPtEkZnk8eMxrmgpQD/SzX2LkULVrMkr9EKvjwLhaxxYHngnkA5d40MPu01dsafCGymOOwTyDuVo40N3rs9LmowVAR08RR+OjnaqKv+yoQtAi6Dj2vyXMDWF0OBXsqDo2MDAsb4OEETRN+IaKFNPoRc55WxwxL1SL0Wucvd2dsSAB70pZmPp7qMy9VP8oLCR1S21RwrJxeHQdgw/7bDfJNijr1NVzkDrBhI1K5RpZl++Z1/fTP3GqH01ESV/XN88rXjmtZ6XJGhteRr/t5B66Qe4BC7oQKl+LvIeUUKFUX0nIJJkMmjuUXl2/uBvz0mdlipbCLxfNmbAkxge3sSsLZ9C8vtPx/lC1GL08TGu/BhEB1ru5VeuuTVlH+8fWfuhukr8iAE6wKlPjuyBMyp7vS6+wvU+Dadz/OBJIraI1G6mNV6ze12SIDgRQu5XAWdhdhLnkDc3lVFkCn09bMGyGwzkSRFpfcCj14iqZ+WW/RgA4FymCxQyVnKL7jl81LvKIVC9ChMBU8pt/jzD5gqe2CsE4wB9+dZUscXg1yAvZo/e27TdNYDSyUwiG589ja8plRtqGxImAIQruQh6pZ558RKSqAbw1KYsJqLiP7kxpu/FlTbqTzSRdpXiPeWSSJGTfrW3HqbDaRE2KY6T4Y43lEKB3tG+cQKgHx7Cl1V1fH7YieRcQoBbGQvuhATha9S0NQnztmcgaCKM88v9Sys7Z1KmteatGeHNZ/qwhMfUjWQPCAQedTskRIwd7UpqcqMq8L1vL2DYquRZ+z5urQiC6YISJtSBVqH7thMlQPnp6rao0Ffw6Si/qFtfZMo7lrDWHtKpUz+M/4HztXUr4hieN1gI7XtsTikDpQQVOYG5Xj+s0BoHElJk9WXZlilBB5JvQpoTjS1zyA2klMtQukSxnCIiLfN5PxkKl2LLYItfFnpfQ21c6x68adaXsxQw3/2UUNLghUEFFtg+LRpdI8nG5JF3d+lhFTpx0VVaqeGUhldGRvkq14KATbpn1opnnJ6a64IYOJP0Or23ao9qDY5jnnXexJUtimOeTWz0xT1y3Q4d0IiV8TLC6lkWnueRgnoO3n45JIQQHQpzn7pe9w9rzoMQvo3rlo8veNsbDTRmFiNm0GMOWwdGlR08qzxYl0EvNfavTGtXxYDqPf4N7GITN2jj+6BCk144gH+z1HlklevNkQqhsguHreqe19x3j7X6KlK4GdP5WQujHTiixnVZtB7mx+b4Hdka0C8fHDl6Py8zzkQTvYvs064yF7H7fCPlXrfuFMwczM8VGVa4ayElG37/tmQwnl0jICE6ZJLXJS1Rkbfg3SkPl1Re3vZiVSRCpjFcETjkBzsQzeUyLSHTHy8YvH4pkxyM84jVFco0heyJpOzlP0gI039n4NZQSWvG4xpsDT+VORtZN4hbZZQ1IrXiaeOKg0quEBw9gcYmywDDhfpnrIr8Iwne+FueTXD6vXCLCQ6FwufmpUa8V4jMARnobO6gC+/ABxGY/TcSVspozjBg/98mcZRsSkfMNIvKdqtIxeo+YnSNbsSHIGKBmngRusPrs6w4aackcY+gtapGI8vW7a6DSeigr3lB9sPkrk6CnonnT672s19T7veHQETWfb5O9Lp9CP2EG7qFjDIrd6N04k2OLNTCD5k4dwNxSG17IjU8FxSKT4Ffc/Q8jn4P+GFkj7+81+dWZuy0Cl/qJz3pliVxOuAxMU/4zmUL7zdaAFGhQnw5hqfKyVq5rjY12bUbf0oN9ZFv712j4KkxgMTKQ7hDw7ell9xCGCuA5KVpcVSfT4Rwzr5q1mgy0Jgxw4E//KMKjn9qqe/5sXbzIQkdbzy0wEXZBqbHKwtjAx+sxzWDtANGZdAu2QmE8BuhDQSBqot/R3QRzQzx5RuixrePld1lWflWgkaBwLvKcmP9UdnbXPckL2a2+/LFecgksCvVooN8zyiH1W5YXiRWVKH9TE5x2cI2X7SeMRshGUzZV9yfLR/RKMLTe329gsrWLsCcGjxzFpywHhlt7FAIWoiiRCtMU4arzj4kJjKz26/KKCGCEx1OgYD4GqY7IDKOriMOZuSw1XfXrhxKtU4ddfqXI+3gMxSS4Y03mc3yvonwJxzd1qXk0LEOwF6gj8xoXkpcyqoxNr8mSQNPhJeWhIFg1mlyyIRUVa05SIFKld1qc0AMk1n8/tCvzHpCbFScXL6o3o5b1EJetyGxdRmppr+AoovtQ03y0G2Z1uUR23AtbxkiZWlCB+g4Vx5eIm8e8uLw2s/AG6Qa8cPSKnhgJ2b9i3UKpVS2mHezLGTKHNsEVGxjKoAdLDrEaj+S6y10J3RdcQ5r70YuMAQKoT/NqYjiE825I2IvS7A7W6BIMFMuf2sPhCpT2RPtirK/ppC+Z8yGrW9cZFfGeHVTIetLNP4sMCc4R0ZBGcyBEv8gXFxRyQ1WTraIWQMJeMDGSfPwy8kwcammlgVyL53QGU8Yx90hXIiaUX+zbAmHjzk6T+yL/PrD3ed61A/eeyOwcWq0oQk30pvtk4VBeNw9vopFDJPJdoQayQREDTc6u4mGbj8luQlNtwtxaFzKTKjwm8ycMUxzGPyj38UMeuZePcJ3EAeMIAULi/3p0LHKnmJQliIeCMYZ1INd06BRUaH/TxlIICQveurvXIehKny9mq4DDMKStEkXVkMkCg25hlPVkEvHUB24FVjp5emIc2XCpJfN0jUzM59ZK0EepptE/CC5efI6I50BMs/ssxB/5mwDprSwVSHnHlcFZNgCp/ecTyouhS3BxwxfKHFNSEBkTayxz2i7U+GLA9jQ4C6a4oqf8Vq2KM7i7ShwtuBSZvgPWVWl6Vu5epxEatszjkdibEt4HtpcGaZuTdCYR6hUAWYn8IA2BqWS7gojfaJRV/jp0NDYXa9XvEvE9zLPHR3HBQLxEckUYyaoJk4uYeaUbs/ieR594MeMI7BJjaOieu3fXZeZozi+3lREEcDMCIvy7mBUaCBHZNcQFuqhRkRSA9X4CTy/QH0E9KfXkrAchjsQHB8X21Bql2GiWDZ+cthlVnRGmLLQG9/WwwYCrE3/q2T1aBnA6a0GUmH5OcDXdM4hI1NwkjW0hbY3NcleOh9Ad3LQ30Jw+pkEo+cPggLLwjKqUUGwpVM9N7OV8UqobRPP4yR2pi+57NKPBV+0iddQfClw1Uv6LubKBX02uRm/bGCtLqbrGTIlHDJ1aVIa9QoAon0fKfhh7nGAXxO6zIevC0c2UhttpiQEXO22i7orrmEaXYXTZSPKA8Z2BFlUlaHb2WDe/ox4IjQpQ07H+kaYT/Y72j0nrIJ1MYoBQfPToHd1hQG6wMCLZb0l+HwH/FxvlAYwpSj3u0V9D5wmLfVyadoLP4j7b+Xgd/VbR9DXrwsUskOgZEWOOs8CdayrparTSkQFVgYDDaMqgQkNqj1e/0kiXp+RrcFsrNz2r+icUeZ4DHbfrvTB8ENeQwjfpY7e8GmglGtllRAuSPxzuEwx6j6+Gn9hFehSfUNmO0ZPU7on1N1SRE0AqUZSjijRkVxXUkRAxmhg7GIjeP7faySGAGzViWCethRbzThYKDx5kXKjtbebq2wMbaAIamY/5XQsJmXndySfoATjUzR+Hz1ehCFvqWLuPQzr0pyjTindDIWDHwEiG5jn0zJ0NLcYZ9Bc2px+K1+VG+8vpFJPQkFnyLHCpMwNr4ZiOorfYQx6gbLy/AOSCcVEQJkWWLIh4TEWeuLDxYSaZydnwEfLjpjXgbrPi96ZlZcbyULIhTAqg6SFiXQr7W63dnJKzxrwA6QoV3TYQtGwRL2/03yk81+I8waZBFXNLjMABDY/KMc0VznMOU2HWIhHUpkUWcmitO+z2tdSp0K5CBtqFUeQzxHUllSuEj3J4ZLRresIbhf4gXB1eyIKPb0TbPbthVUDG3UVQQrChJ/Jz+E9mGRDQLtN3/I5IA7gqsqII1dzer78KAktdAKEt9S800p6puqQazddq0/Q8Y6ycVjJAEYshQunAjM0ev/TG4j4jjrZjjegNKfUvwKcHzYyNSLNSbqcNRzirL8Q1cGDANFsj4wl+lmggCD7p9G++uwtsWZ+iSkjzr8Q++A4zFwNB+WzxLlfSuQ12eXV0tEXTm1MW4rZ3IQO13DxP1s5m/TgjC7g5NlsdUrprBqvxSeK9ThHjRg+TKp2bYAiqaxd31bvbzu/r00QW9U9Tner9nXn7trZDnbfGRWRypXPCGTTH4u39vgO79x7mAglMMRV5txmFcgcgGAtPHF3Fri7C9vZfzNFAUBc8AWrAoVKrXlJ1bSelEu+j91ixD5OqZgP6upPQLTgASGArpU4AK40TUDQnSl4uy4nbTLN8RQKa5RJYvIvGXOiFao/1QYczOZWAS21BiEBcvmqfN7/MsjcUwjwMTgt6XfSGbGGukP+tusVeHuKA2XvbBULQfYZpIiL8fxrOKvYBTfhtRQB8IJdrYHj/+eMn0JbewYMQ+xrk2eOYqxTMtImotpuA8afG7Xu/0XkyCVBU1ZjFkpARSRYVtl1Se8RWMRzb7nyU0nmOV5GS0VdupQ8Qz02RijQWDtp/rAwlHpFBT6PfnnGM14N9oFpTc7wLrLXa4W5qlfpGeuinvmP54D7wqSr9G0ITNqFoilyPnQvDGCv2wnD8JN1koyOAT2O6SFrkdyTrD6NBVG82kOdPCBpDk5IYx0J5Xq0L8dIw596TwK1khVAcdBG4z0bISDg/ClFBhVrwHid3QX4kKJZA13V5B5tishx+6syMnkmowtM6w9jQRSxjaGFJu5fDC2SHxMHzsAI0bArNl01J8c3jZgCSfn/PoZy8fmBtMuYwsvRxRUFNmVKGfGnvcdApqVE9Fo8d9iHK/tM3UOSlmKQpeb5j7Komnx2O/By2RUzKvSCoJ2eKoRIDlcSQPJvRM9I8Hz74st96mmGwmhIqqkrrZ4Qfe+svK5mFcsQhRQg8vX3vNXifRa8Af/uuGksDfv+QtrLjGnZscmMp9H3lJmO++jyUH6Vy256tDjrSUjK2RBatgt1FJaPdYpQL/OfgzpMD4D980WvC4R3HIDpdtd2mRP2ZttmjBhAKwsbnAnX/+Axvg3eVnUdB8SDPmKLPibT2t44SW2WNZkIwhTH6CD0Wj2xzntFsBp9GJW7gAWWAymGClQQsKkbWFzFiTly6h4QW0Zf4se4yQQJymdfyAR0bzgkH9SM2OttGiNveotV+KcvanThl36/sNBm0MFSp0MDQvO/Gc5YMaZwvW6hwe32v8+c0WAMXrj/wnN2g8CrPPJ6P/GxN8rCIIov5TuTMD4yLeUb8t7BhEBPmL0YNWG41VQMGgsJTwzN50QNz4zkavfikQ37ngPt1OUH+TRVdXKrvwMQvD7O8u91uvCnU8YQzKQkQwqi+JFYJEFow5oVZlrPcJ76d16M5StLuKjCkZ8/EvN5+FcKgLuRK1m6klX1qKxLG1EjL3ORFbGzsECoHqwfzVsV+lWwQTVrvWmEZ5/CPiR3wPvPT4iNIiHwAoPCBip+AWBOmBYbyeMiMvC4GcJwgLAcU7cqag/JlnvnO45qXKtgptzYfilGUBLIdLzaYlsmwbXiC/8P7wof5rwwJnFaqnbcP/HpV7Q9sHMR8dGzw8r+n0hzK3v3wCvF5NO9eP5T8sdAz9ABO7TR1wTxBVvMqr6a64ylrs6KMwENKkMLQhpjJsh8F+qqX14ORJdUPE9R6w7NZKpp4VJnY3j+S/aI0wHl7c1dT20mrrKMl/QoqirKSponRv/aBnLPmm5qb8pRdC62wkDLsyz1EqJzML/Dloaqa1balI5Fkr1utkcqIyewK0xB6OSKlNvfq32bp9HjQSn362o0V1GZL8B8RMxy41bDpbNcARWGc6SrX/ODgEuy27wGnmTlLroJvqZaRm91CFvqY6/XcuJ/nCafDvE0dpNEoegEJXwJOj0e6DcqLyiH7nY21qElokBOTaQ9WvAonGKMry6mASll3//HegHO393Kpvhsh2oKXXOQ0omF3lm67pzB/P3qI6xsNQp4a0MBTyfda6yhHjxTzWYawqIJGK4xUCYAIS7sXrDChUmu7DDFWadWg1s9n8Y4fVzwGCpbXSpJ0j+BRrEVg3t+ZF/IZeJK31zBRUIOfaSTV+GjPBQyCRGsppF0KO9u0456X37azvIuZZvhV3l4dTvAFXyzjTkodJ1OyNCbCtqMx/zBDbOXgihXPqwGp8i+NI8vf3w1L6Klkj1Vg2UfwLO3tI2rbfHJLzv0/VZOcmkEsWKwQm262yFcCaLwtNFdZ/HJEiiHO+Sgy0KIh0PY322Ne3T0uuH653Z2RsvQSO9L8c9GMxkqpRc/FDKHoWLGiXCMOfteTuZjdKtB27Hu699ZjRfbBj5J/2VvkncJGSw7Upwa3pMxxN6UtW+LuR/DR/2079faUazpx0JFy0jsoZj54kKPiJHIuqXIK9u0wkqL1JvCaWkbINtZCOtreFxxf5t6yKEn7BftqTNpYXud64lf62b8nOwLjAOknqtgY8HQsmZQa0lZh1FVL0vHJgJxCmB48vbgxXZreEA1hrf2VNPAPTrQ82v+UDXTEKTC6uR4f7mQulhGknKH2r2q/h7jOUQ2QJgnqOfPmu69q9lsMpjcYQPQGVPcQAqThZt6K7tYKucLqQQKBMNwBgOHDDwPqmCKlOodEP0WYHZsjm1bdFEAWaMlRbQnI5iAZynXHmaGWX1I6E+f4hzkSIEQlLELp23qKl7cDA7Z507MSteCir9Q6IH8B/u4zG9MvRtAZS3qfiEhWKpLAl3fllEvsmDfDkHfkiL9fRQCCfMmKEzJCG82kDbr1BJ8BMJgga3yxMGNwz9u2HSyyV3elyUasblSMdQofiGWZJO8tI3RvfYjBMMTvzoiUBR1KgjjYD+arhjIw46NyYg9S6A2X+Bmg51AiHjhZFl21Fcz7LZLMp+1YToZoWT8CcfdmN15uIqg9zU3g2NWrnXViM92smDtsCiNKle8teB8pKXLn92tTkDza6I/+C27/GL9EtWnDykXt8XtHhGQO6y5fVWDo51hXM9UIuS+wx7NS6plD3YRvrRRq73wyBdtvdNWZzHPO9q1yW6HEsc+wBOLldwFJq9r0vOAoctztp8UiXB/cm30VEQk2iIjsngXJLbbDtKLM5dwwa6kWnY2NmdB0pBUlt8MyrkFiSUGnIelRFGxUTJkZ33AD+cmQyBxQxKg28RtFpssucCPe8xwB/iS6TtwFJZPl8gb0zpDJxbqkUETN5HaP0zRgJ9EkR70fhO61WcSKpqInCOFMRvq2g6Pmvimz9DoRvP9cMJT9gnKIAAmdOPiQLnMb03kc1ibjwHTcE8DkuuIWOUM8r2S2WLHfS9wbOMEs0Sr3/0X9jhEOV3yTK0wOMjUQdp5WF9hoLNqIaIfORA4zeOegFcv4BM007QZ1mmK89N63954c4JqfII5MXrgA+nd/uVx8/ySY4f891PlspSy7yr4A/uyfrD3zzzCjD8OHJEuICUGEu5QBuJQtQ+l+/LFGJieyIR6ED+DZkT23fT8aI/unkntg+u5dP8WV9rEl4dOtuVTZR12ub33RsBm/219PO5NC4kfamFT7w5f8+ibGiqgi7p0alZD453sWM2ErkFB5Ze4OS24RghTQ3NsEeA3Vse6V2sipfdEhH1+PrnE7lM6AkRsutBH60EQ+EUdii8+GmpCLFmg8eJYxH0ugDz8oNcgMqsrwhWEYMx0iUtuCWdJfclK5Sh9RXWIic6rSY2kwFzFWOxvltONdz7Pd7wh5vNx93sO1dj/SISsqk0+yYDvIf++/N9PxLZYeghS1+Yyxh+8ePG1Xw6XBnV2T3uqMGfVHJQzzTPZYGZy0H412A2RtvBLV7K8LHNB8XA7Pz4l1Dgk2dCBFlNgWdkx1wxnpaJ7oKgqdqDx6tBCEwmN35mH8lP3GoQWbibNToFLhX94GR0F+u3mpPs8yZejLckfbttrpka4a20xejYHkDvH2tsTrTPixhO4lYokvVf3C6njbq13nC3VKMa3eDdLZr3JK5VGkNkvvjdzBokrLXsoATQDfYNNvbEuKsdkrn8Z67/fyRqy++5TLhd0R0k7Kcs9UCgVCeSJ7HANBgAbe+iXZ/iJPFsXjzR0BkJOlh044qC9FLtmdJmGBoFgM4vvdQkw7fCSZzYwGCV6JjfsSuMvYjsOEzZ6D8aiHs8dNoY4UCt3f0lBrnFyu2fKAeVsN0j089D84xseWvrehusj9D6EpbEXOGHBY4fkaEtyp3GwEByV09AsKjTwwqwCPE2X37t6aTIq8xIaWG47Uq2K7ClIQISfMiQGx8GzAVINzcZTWkGYEfMlVPqUNBXlxCWnZlOMl7JwVZ25woJc+AKXQnbVhY8+HopdZKSP2DUDctVwlYLbSIDpQU6RZ2VeTO0vigkJWcHJt7eaD++3CHi3tyDfdMGBMxSa6nlNe/obJbT/wl/HzASsHVVxRVRNhh5NwkKJ1EefnhA2en6UPZJn1i6SYEOz647j8P/VhJbk+Z8ftQi0/1hGXnhXxZc8yfnVCysawL3l/Hm7sycFGalT19cxBvWHhuIDW+rjjyzxWWPn6P2HCvCDTyZuAUyTMaAYDOE6WbKqSpEPSg8ppgj2OtLM0N/hF4VtfMB6bCbU6R6o+2IVVItRaR6drZ8PbSiQmby1bvgpRdbiv9r8aWu3D2n41nKpj+5gaFklnAN+NOIgFIRDWTrjLEWK1+Ys9GDs9kKdPXEUJ3ME0+CAsU9YjYTizE95cNVQDncPsJh/xhg2mSLcyM1fDBlQhp8kmF0qiudVFAg8HHFEFOEDuC4i+AHTBG1297LEXZQmX2Gur2kjy7m1Rmgp0gKszs8//lTiB/A8SvtFFdphiOWEaTRHndhbGtu6GqtoTMp7vUN6KrdiUDVrNm19PGSiTu+s/E1ankxpU54zclwx5Fuo2OLxH9JDRz3qcUEWz8hdR+Rz5WHz4y+whS6FmHBLScUgFum0KJ4lpDBfsx/DGfoTqMQQDM/53OCu9fQHqUUi7zw6nTGWBJv4lR6/pVSHltS5vNEH82m78y1A/Zdj3W2HG1Afm3Jwuy3b8wXxMWTRhh6YEZSH0njXmjIjK34UOr72Zj7BEdBpqeV0IU2fIR7TOVYR0KeIGivUQFB9d/KYBcnInzp0ZYg6UwR+lY6Ji8Fxs0eE7yVWChtebpEv3CiTKn6PyN86kxI1lNYP0F97fTu7B9g4JmDDrKXJXdAGMEFa9IeTxznSiQO2KzYmppAGlS65hVMZnqb7Q8Lrio+sGmjgEiczvg5+uWtuC8jupxZ9CVwr7USm4tLx7DoKbt+563JNkqOcb/rzeKAniqjvfFY9doB4IGqI1Id3m8ycqbTXdHXDp1DazxqFMp4LLZys23JcrEng8nDn4Hhg0TShRTzN36OIhPdXchBeQD7asHBkNySehuXdT9WWjS3XozpSGnbm70FHpReynV3y2m+E8Tx3CgP3dELP1IpIjgpU/N4J65ZozKxoJBeM0hRMy5nchuzbLw6BzEtHUlaEOPY+rNweINc+V5g6+LFTErZWXXn3rR35pr7ar1cYL86xp+IDzuWEKyA9y57tghSU44LBewpclIE2nROU6l3OznIJKKC4/K9lVEvam8qsO6jjpUkFa/qjThzyHxDR7Wz9a7y/MHLMf33EMx4n/b/Ny42v0GRFUvG0rIUQLZ+nWwMh+zqBmoIBA3tSRJEMR7mJ/k/oLOo20CvJwRaMzghAjk7nyGlZOC5Hy8Dy04jWcA/C73bFQBVwR1lPgvYYXMpd/SWA/YxJzgjgcWT2nqZJg8r1x+TMB2JjuobvImVjUCzaMmGMyRzMKRvq856XzydEtAexZzOYse6r4weVfHuG87dvqidoF3QFt/P6KpaxQ/EkZnA8FDwjg8YrMy4axlN3TTUI5JIrDqij6R4cpIGzPSik/G9QvtUleC6l0r8xexhTO4QKCX1NMnnQVuOlOlf8nK2aDXTci8c56Q+NgNJO2cYK52WU1xW401iNmXOZX9ZNPtkrUIwXXyOD/Ms8TF4Oy5IyHI1e3jZ430RQWHcZAkroRzAGngeVtTfdhaiPyyvatNGrnGcvt51ctSqLxtzR1P3YSM5hXDeZCwvLfsbYBz0ooOx2EexVkn9P2YOXMQV5ie82mqEPjj39++EYhfnulH8yOQ9+WT8HzvhPG66Lk2znAZkPAKWG7i49kIyIAO0D0vCR0BQvegXwWw7gDt2yKnb9YMd8/HU78rKPkX37U7jWmL/kP1NoYx761GO1iSBYgN6P2yWOh8rIZQyYCgyuHZG7X1mpVuNEVfJLvhyOcO3/nmOP2ckKR7AbobA4FGdmcFkXHJOLFRTvagIESdYdtybJxPqdbshpqCicF2Fz/6jWTNP/G/pXoc4iNkzIOb82iznM/ZrF30Rhzed6qiYZozeIsiRS9/VxYF7iobX9L/N5Xa7p3YeWfTN9djUcGFCLNpqFGoctc8txuM+mGRDedy57kDiQRb29huICuPuhzhexiqq5vfZoslNRJzRF+ZrHSA1NACYLiWj9oYlDJmdyOU36mq9cUchDjWcgstnO32RwdYK8FCncqvARnpLinqIGGyKOxrFXM4VjkdK0O9Y3E3drRvjuoQjFjtXmEmQVu21t/BGdBaSDhaisYxswug3Ncg/O4d5guw19FPvKPkoHQyJlLtBJQhMblWcCVAvJjywo7Nxdv3CFRT52qJ8yOjJgppMatIwbgd3igAuBcZWrfRDUqyG9rRTaiAi89Z700bfRLT2pydmU+Rukr3qpyfmGqufRPtWGzTIv3qHn/j0lgQgUTX7K1TpL/bdCf5kQY6Eu3a28uUS8XRMyD8NmILwCqn5dJXjT7lIaftjTnR2FsKYJdln1lDL8KkZZNAJPArrw9tL1qoABK/dYw2O/GTe70Sc7lZ4noZvd2mfRhQMBwUiuF/HffDSFIxtHOe9/IIhhCfQL/sdF8bHphdf+mjhVdJkFOezSDlt40GHwevXkEQv3FK6txMgsDLV17/lPh9yccrLA9FTnDYnjEFwZJnc7JrVhekdU6CqLUyXBbiCh7gomJZn1hcTdFmy3J+JNmqyDWxcwyMadvVUaCcW7BJU4VtGDsWi0QDb/dWc5hNqFe93de6jhFwAHFz1ewtIkDcZNcjEEu2xBpvnys2lOPuRp22xqKaFOEcfCkIurw62X+/jiYJ6B/e3YjNVq2KMatU2VE42bI9dnWTFS/oDL6pkZqSN/NcH5esVMkMjzsE98FbPk5HH3R5eZbdh2xzxo4NF9qQMy5bsfj3F912kBVD2FwR3S9RPpixM8VHygJEYTuWoY7IhMkAD1vanMH0HGqhtkI/IneDQdXEzh7UE3pG71/Vo9pf1e4cgva/HdnevwnkN9iZJHwcaHCwsIm7eUTZC5gXR76fky762SWJrn/JsOmduLE2rSXQE7mnFEHzAg9LsPqYXlqGBQDMSyph1s6lhcbJtdKbucsHskif/4L4edaUkA+apoIjR0kEUuGJOOe+QDyIy2f71quIIKy9By4GktHIagcMn6GHX574/g2fqxMw76O7q88ZfDZbSN8/B60UjRb3cCari2fl+0/mFltVuB8hvWDcBoCsl55TgjfXgnG830h45pY2sVwcVPbfGq/8Y4ZPdHsUyHFqi1N5o7qNdcl5eUl5wXPFsqiYlfZbAAnL0FIUx4+E+uDBIofyGtdgWymk24r9zb7BnwUY3GuuA3jcrcNFPQtvSSmts75x9yDZecOFrTiWaHQrDw2fnQ8SrgLpD8+lnBEKmX82+osYAtaeXzJMrnwjd1LqUiNL+icPXKpjyWHN7zPYvW1YtobdR9REaEKckDTY9qurGiVE3b5CRQ9OrDrPLf/rlfCAgXpH1MxdSxmCsikJtv2jhSCrnTJbIKrt+GGDnYBqqthQf7X/trzamBBcDAJ5IAfwrlasoSPyKx1v6aO8uA0X/bitknXRPUTNP4xtQ34jFUJ7BDS4/i5hDLfygz9eAURyfqTMDKZSZr32g1PBfHWuv4sx1tGKuNqRpqMStNFbfSFZ2v/Y5HyVmyNTBlCh9STNGbDohxw/YF/6Hn5IGR4aPFIBoIHuuJI+WNVAhT75dWi6ojQYv7cGRB/ssPxkfjJ8JrTD3OGD08+ctXCU6524NFPVNUG7BWKUUnlOsP4mLawpIrfnF1ai3yp6Sybin4ynMF4jxd0+n+wUHCaA0p+rJhdjv0yfRCV5ry3fqBg2rkD2wGyh4Ge911UxarLTKnD5EagUYGNZ1X61vJAzBU0JbbxdEwtg1lr4qM+4L7KiWm76FzoEclrk7ZnTVEujJLXQDTLqnUIKNDm4Y4LOC9IEYXBBJ2q60ewCIdtDUHxJMAj053Ldrcf0DugFyk1C9ysTD9+JF1aLGmrU2hydlEzW/AT5nHs2zUU43+OcvVUtA2e0wACR0RpuL6z68xSCEWfjYJdF+YIw8R4Z+KkWat+HXj8yynza0wmJBIccFqhI9/uWzOzX+OQczMk8WU+UyutdE8/ISXYC1x2ZYcE0OXGy7eKMl5CYWFV5puI0P6uELgG6mbyzF2u8P6LCuEnsPCjKaHBtJQrnr1zYEQkZig8Ot6aiO75dea+tqCOj/VhVosW1V5irTznvrpZUEwD8/dXEd9m9xOursgOQuNKgHsaCi7uwlSXoPbJ97nyZBQPQuc8AZhFhjuv/CURh5XXe7IMyn+wgxxIOgUiQzeYJ+xISX0fbd+e3KJ659BC8V+gdy11b1F8RzfE38g2T/Sn7gRmu1oUAzAfO228aWEwIT3OrB7KMHjJP6xhs6AslTq6NUwc11mAE3DOFbgnry1xmg94qpDWnNnjQab7nECFiRnRpykpbAgLzC20dsHrtt2/Hgb0qOqRVT/y1sSzI5WchaXGGD1o5MKI6aEFdcG4/tq9AW0Y29ffb0WlylviPY30063R1x8bLF6A/Ac9Ve0mXTmIWAs4ZRNd1mRlBCQe9MgncTI8Ly7uvx3BJeVZlyzFHpXQw4PX2SzzJkX7lSJ2s9osvTDAF+jxFgEPRD0wmYWSZVlwl7MM5X2xkLF0X/xelH5y+3VrWKs7H23XsGBjwjd7PABO5CZTa7MuzVKOZA+wz+fZOzi0y0KufK5cr8vVcbKBULtZwn3kvDUFM8GycQp8/R7nAyYQ+S1BUyr5lIKC5ySCuMr/fay+VKm9OdG3Sz/jrmIf8f765YZTEhrd/G5Q3JnmSXV0t0RgLmb5iF1aw/Kvqb784i77+P15jn/2pj98wW4C3C3jLh3ZF6Pph9k8YAkSfBxOjPAL+lklvNH0DTuxPH3vc5VxTSvcLU8IAYNqDyRZE29np8kT2pm+yg5/aeo2t0g/eDBz2mJtRW8a7P4U2Pb5+eIDeeCuhh0MM1J0r6yXiGW+usUcKpEqWPiWhG1Wv5YIPeD4heoCj4W9OPb0IOupZJt0yzPrS2U/xtBzk/nmrT9iMila2yVWFro6+/4ZVjQFEnxjRwDZNcupJ9B6I87FPmq/v8MqqbNvH7SHyNPLQk6ijLuqvURqx9GcwD8yKYftXIXqN8jjM5H+ymsx1FzdUPpVdVx6NJSbLPoZcd3pfDelpsxqPtrrH6CCtI1td1aaqdoomCq9l1TSFHexIID+56oiyC+5tuR4DGPacydaUn5pWeM2KZcXhnkigQdnJbo8ayi4rw7cSOwQ5NAvOR4pH8Wdm9C5u8R8i1Dcg9ih1pvLhq70ooPqj2RycOlYBUdPNWyHmr62GkEqxeIvWS0MUCc2yp+TSq37Py2O+7vAbSHVJ964Hls0ZgpjKYVz3VIzqICOmwraPSetm5u5HG4Hudg0NWO93j7Sff8I9oU9Gh7FASYmNCCrN09oNy3YaQ4TFSA3JEeW5/XDvA3i54HdmtI4GxXA21QQsgWwPKvp3GP+EDO0f1yASUtix+TYGjLOD+Jj8kuVD1+qRaNqvSON/8qSRZZd4e4eiDwd4RcAIgrmqjqcnjJl6ikR2IuxhBHb36enbIoBB3ismPFf10tyPScZKd+xkqmT3IFR3Q8Nleft1LSS+61pTPVKN2MgkCe1cHvLjlGDgORUVsv/Dw0FYZ0QFN9CTs9HFn9bZ8YkBIPqf6DdkGTVvgZzelLQQpeETq7NhF2tlzn1tDmTLPDOBk/7k3xJSUARXBfmyxTk4P7HxT/dcy1Y2lT0mcwp2w2PHz7NoKuRWIYRKHqwXLtmNNxvx9b25CGqUQ9Ae+wQJx6xoQYFgIjfGkpHBsrEmYu/Xvekq0J62ojhIqp3waG7Q3Hq2+sX5eZ0goc8/6gZcbvjVgDXqzvE+THIAM4HiEHoXEtENl82/Bk/f2Dssnfmcv+mdZ4iXcUoqNk7ogp7EclJvFyEcqtG5Dsc5tcg8dAwdp2IT64LQw8zsSDf8aYnKGMl6AikFup9c4W8G4kJ+4UjU3fOpBuc4JlOc90VHTRJVhsHjFCg1XOSNNs+DWzdKfQtV/AelyK9kGXx93WodW2BHb2jX8rvOO7yQDVvcq11Z1PXkkLw4RiIB4WHZzGxRTBWxOE6SqYOYW14wcp5jjfXz32Cg1Fk1V6DVbsjAYAtHmlUtswMG2dco8x3/2ILGth9zrYI4I5YRtG/qIhG7n2LaKe1avbybdaY/23sHpTqTLYnH8LtS/91jlBgP4Z4u1LUd+VRnNh/3v1RuEJvr/SeySDm5I35f76qw64Svb/u6Zv9SMUz3IWvlu0H0P8SL3XC5KGq26OlFBdzuG1XUvdS/8G9d7rWp05f7ho7icYGpx9iVZIIWm9ryBr0oPUfJ46fK6JBGzPiYR6FJa9UQ3Rrv1Hjw9pHqAeyYaiv02dBHI3zwBszfWiM0RzD4i5fN05HkHHTY8hRQEfF80jQISLmzgMKA/EgQY4kmRZffUWUlsP0ZXD15ZNEQjtABkxBV5VzZpPYu7kK4D7meAXyhsRfJL+OAJ6aaDQiWHlYdyzuUePd7bIpL8wfXhwFfgXilSMCPC7bnHAfOVMEWAhivTbN32UFidkZ+2gwQduLGx0YcRO5JNXxE518POe4n4L5EtuwVnOE/LQpsA0Zw42ivNSjAt3/D3QIMRXc4UK7TDdseg8oX7igGcLkW12JMDudYTWCt4UDUtUJWqO2YnTxmZNpO96wzIIYx9cvDrjKkqU9TMb24fOeHWsk/kYamQZwPIRRtdFczgjdhUj3U/r2QAjUlB1DinTKTjqncgerYQbA6qTMNgD/vTe9plCSorm4l7Tj5s2/PqTXbks/0rlH38KweeMaadnlIxdOz6w4JYAbNmEU6vleYgzGFOI3wUJA9ebWQAYksNh3l4V2LY13qItW1jAQGK4yoRZOV/tus/bSWntJ+8+GNq+lxcYLpJlwsr60g8+3eqOiUU5K+jcgS/FZtcOA/Sx9mO+cMQjJqaBrf5HTNeewk5q7yJLU3fH5s/bnaCtBaeZoRld8QTaEw6FBYllelH0jfXPObwXmVIq2+d8f6gViZ2JIVctHOSanwjxo9a5OPCoqrP40T9gERIBqziTO4hZuBJI4UZ4uxn7Q6OGZTm5NmUqmc3SlJWW3NVZXa7rDl/x++lQ+oXvCxLN6Uj3hvmUxlBxG/6NMFChiSihS4jkAZ13BIxkCwmcgTKQNYu7qt7JeZPXUtjsYpkSAC9EWzlVI/kNSkNJVmLrBwjs6Rscrjt4MNUlUoWGpRj0RKzDdQRMjrKWQ6D7CNKjZVMEv2JbnySsMWYP72q0nSY0YUNKU/q2FS3vCYN00wKsnSx7y74fXnCoZe6vzLyyG2/QRjAv/1u1n/ppHU/AIhJz4/ZvRBtR03yHH4digOMcr25O32L5mGaAyfb6VDdr8eSRw2hJLMqIPG8l+d/Si8J9eHDGAXftfbWoWaJxJ9wDEDQgeAlRvewK5yzi/1lXu4tX/02oMd8CTgFjnoJnB0Yq87HE6NLLWk/cjAs33PQPMudfJ7A2sfcgeVVK/4dHcd3hGP2ROUdiF5LfaBqDxDbJj1ledaowW7jCJRyd1R5xBMPHKEmdM9he60nb5p+dstNqOgCM6UXsxZM6yYw97ZJrSLJRwpdmOKoJ3IrksmVzKTxutdUackfRdcxFvl8U1E5KSSGpszHGFv504ZwN6yNA9IfkF3N3zrtET/Gc1ahdsBW1rGzwaE5pPzXUrXu6w4nvnCADScYDMGieujkpAmkTVhYZP4nGgLJrZ8e8iN30455LpgTYVLLffIgJHqhfyooYa1oC6fz3aB7/tgkcH7E3iyR0OenKw8yvHWugAP2evwbb9bpoa3N1XvPopcUyzhdghUvZRLtEdTpnPL6zYChl61oHDSVqJOpre6gcCdKMqEBcE/qI9N0nrYc91+kL2Y0VVfvZc/lqNOsh41SgPsJ8L0pfYG6nunShs/1b8OBnYdVwX6yE/4W+QGIuT+2mTuqnHGpsvV7V+uw9SDbSV/3W1BpVpjk3NzVSa5ggwi+Bl2ApVrvLl0iGM1ECVjkIX/RjDkFKQwOdzoFK3OUACLMTfzGBwNQB2ozoBTD+dK/SEGg1a3d1zgl/BdK50iFrBKvbKd9hmeVkbcKGPOfyG0hP9t9KhB8904w7kFLtqT3XiVwJwz10le6dTcnlxX6My+gkl1Vv/3olKORfxBb0oB5C0gOCvPohPMEvMSNknvTNKBRdmKCpgQ8oM0iOkFeOxKtfdlZVXPJW5JU5D4UaDmPSMzmqYFhZ/SJFrg5GpIbNRHFI/YozaT54qzFSzwD/A6+2bIPY2v3HAasz/RUqJ4CWouhFtqY9RBMkosQX7NFVG+rYzqdWhxh6/g1PgN2njn0UbmHDmBQiqidakQT/fKKIkuYhqw9f48lrKfbqqixmBOu5uFK8ZJE8tqGqmIsc4UI22h1p+pAisP0Bep5dka9HE9bX0X7A1WVer5dBb+mFz1bLXH1rR+2W7t0/r0KeBQ1XRZEO/c7jvsRP63NJBbRg8IKQI5AxdUVlslnMbzCfIGaooWRaNyh16Wsc4QRPWzXfiKbl4Y+lkWLsz1TbI+flLy6lB3iFZA4NMTTzkhY1DamInbcqz08OkyEeLASwc3UyafTudnBGmnBXutqylDYAapDO0myioDkjjHYeQ5sJx37HTfy2RT7ddwSer8vmMzk59s2o4Rtb68bAz93iuzh66SBF/9kCs1rjP9gxHxGW5cfXSaZCTMCERdHtiOTKb2EC0fPROEMjp0qvU0yPb2MOSzGW6FF4wAvZk74vrvukBb+v+Megidk0LSIndzocANPf+ADrHPB0Z4K6/9rCMc3tyNBEKuHI7Y5CYbH91WOY8/tJWTYdm4p3I8ISliDKOSvUN6YmPR0DEegwcggD1OCMSYdMwHzeY0cjglwYKyZdBlb7XDILilyB1HyDQ5JiZRs2Dh5FKBMzRQlQVBM/MFwgkbaS/82hgEfJyhEGFRmGgY0+9FdJYlC4ITM7GUAdkYxRR62Sw1aGYUDDWPvTFgUERbMygDUXcG3hAH+ro+hwJKE0Mre7cZ0FWqEztWjrtQFdioiLxdQDdBSzf+6utsFCsbxLKll9LR60z7cxrkA2CRPVzzmLZU3APCLO0mmucUXd1mwc2EDE1JvAHs6n7x5HTK0gbKyJs4jCRBj9wP7weeiii2G3zfR1XWX/RF2juekt3In6SOkUoX3sZCnMQyjLteUfqsHSq0mT1HSA35EmGQYv7Dxu7BhgSFN6JGAtncjd1c63zmfps9ZCEeSUWVLrSSzlyjrSPtop5B0ZnB1tXJr+lJNbE8tWM6pvnjwkO06710goU2T2a9JDOxdVd/mmEiRf1S6tF9bRPwS5kKBCwP9M6jfy3xUpNmzIE6bFZ4qB6a1ZADYw5YNz9s9r0OndA+f5BZUlMmWjNqqmduJO7zgZ+Ssd3YmoIdnoMHlClqA4827xPlCWMBNGV0t/V/7dBvQX4u1Gnva0G8AzWRWkHv1ZtNTT23kd5+38F6ejfYkZLYK+ZDVKs1bgI+saYxD+FmkEyecwrho7cdzwUfv/K9K9H+KrQQ8251bs2fBj70HbNXMRzpfAgCVGTEMYgaz4vMFCFtrXuf/wW1KneIIi2X5/4Xwkm0L3q5bRrjbsmPcCjLvvaWEo9jBu9X56CFNM8INcWcFMs3zqr02cnqRdWivibb8eSzOzxNlJbUy6XrAG2TC92/n1Po7lYaAEKNovoym/hPtoXUUsFPJ9SsQs8L7T2dxIAt3cUnIp3UH5K6yJrpie9CtNNPWaG0j5A8ZUmJdLJll0wzWl/qcIl2dGeeY4ymLHt47QNwWRX1V6i2T9NERPerHX98NqC2EFhbuTAbOZXMlVd2IoYvtSDQivL+9e60TFqhyVDAFIC2d2rjrVY5dAQPpdBOfgejtu7AhgS9iqKvXZpgzKdrSgEaxI96msCIS1u1u2mgF3zuT9toAAh173x5sL3isJ1N/tTfg26yoDB7jx8FGLcVa2VTn6qAElbydgfAQtQtV+hZJOPgWBSAXUWRiUzHM4qGNXg6DmbTsKXdBbXOdo/6NCV0K2LXxarCCMH0WuzpIRElB0ctcU1mGGoF3N0Ef90kTqySc756iw1gcUk8W3nljADS6GrDRGzmtb1qAR5oJlxv3anJZ2pqfARIng7+UuxXKIqtPBfA/vUnAwUwZLoO3cLOSK2wrj+Zp0wAYnqwb2qAX3lyKKXsAZNheoF371IyDHbHz5VL8yPMvBwhODW5gUw0u/7VEPBu6QJlUSKsfXXuvO6IIcZ/xNXhtWRA1CM2RZE2edOXjOnp7ivDcBzRRh/QI0nknVlfBsLZEROH7IUkupYlp33t1EITNQT2Zpz36BQu1/CiCVC1olxi5ts3vliWYN+7OZFR/+xqCZ4xe10woZpBtHa6B9IXhjHT9pEWU84RYXfgSa6iHu6P16z8mwo7Ont2YxJ1CGmzgnu05lGHV+NMR9YU0uTEO8EUZu0B3GbOBVQay8hj+o2RTEOvhxEE9zqKYFrTLZIqdWwne3nEXYYuR4FGBWdDG04CgZ5vPzPhMXFQmOPDohkF1ny9KXBD61VpISWniI5ooO6MsRGBpJUmQbP9fl2pI5V0Mr1oEP6YavMdOLTXtWtj+EJOUkYrijTtW+Dgx+QPKp5Y7Z7lAbIrHe/SZyfe+VwN7X1VnvKCbf+mYskH9WtikPACr3W3kAMVZbEhxQYbCIcFi1G3ryQBdFbFZTD0PLqSosqR2zz9J+LvKJWnGq6nSPZ+yk795eJAz3WD2hdELDplgd3p9qcQAQ3oXWZR+GyS9FP3zpM/l83kcEDvXK3E0NgcfD5dbViTsFQxToUhr1psHQjGdFd2CeUqWFubwRzpOS3jg8MKnu1l2INGIzSvv4x9dodjQR1v9SikTiutBMm10a82eeDBU8ML0xwPxgePCwuYGpkGiHv92wcer2ER2l82EAdVhRLftalMXRpvoHD8wPYcwt3Swk8U+CgGAPJISodqcQUlvCMMeE5GVSFGs21rh7Q/k8AeqQuKGvDdOixfT9CmhWh/gv4f9dsT3jnX0DjKeRL6Mo10ndmzjgjA85kl5zY2Al8mn8TC2vQsJFlIPpMiooaDT3MLLCXMMKZIBkn8qX0mZw2MgEj0noM45c/4w4pkwpfzyLc2Iu8T2CsHD9nSK0+3Pk6ei4BoJEbh2c7TdjQqcCF3TOiE5sUDeEOmOK4ArQx2QOQ1mhVP+XXHQ4/4DgHz8QFMK0kpsba2o5Zob9AobYJhnwjGRXM+vpR0+aA1vvzzqUvdVoM+uiR5H8/nTV7Kh1voPmsRlT8SolOIQZ/p3zLO8PXnrmhwMHJP4TM5Ad2S8BlYq50/4fdijRBRby11w3k8r7LbM8TguSMYpuTsqs8448YVT5uFZ0EyFdBO/NX5R1FzEBS13uuyaKqkMHbuyu+umE5Yo5OWIemUQ7MjI+oZBEai1HGNHorAobDgJyDK0ByCR7M0DHaDKdFGYT3qf0P+6rtP0lv2NEefX9S63dOTNDvBJXzR9hvUGs0GpohCzec+W+YQ85gYs+VMf7sYcssyO1ss8WO27jFTK+etJmGa6UmGxTpjpka7YWTo0CvF3JkKqLutjchxMD+XuShjq9IsHAgaguyBRnVUQ1D2fMDJrP5kQKY5Jm/opFS0E1KbAaLv0dm0GwsxVrLblZM/YL8W/I/AIrIdJ/yEwr1rfO1WZNwaNG9X9/40bOfJG/euUA8jY9kuODxsvq3TgLoGyimRaqMpzbYoP/wCjU1FJ4wkJoGgLfhloYXJ1mtnclMrP1kxxMAVtj5NT190HLlt7IHWlQ1jiDr2Poj7XuzVgzt9BKs665MewJAmEpJbKsJcUPZ/XOPGCNc8LiaUnvZg9yAf9jLRUXZ7nmQjb+sSYY4RiygCKL2mMY7mBnJzlQG+PcW21GmkUrW4RQpm1c7I45cot/vXwnsHKL0isPfKoDb1dOEMi6XfU6GkQHi/Qsyz2179f22rH0gAS3t8ePJIEXkxXzCmJotdlpbZ9j5WnxkUXTq78cKL/JBsQkQ77CAbe+X9RiMIb9a2ieleA00Hgkj0Ep6NCiSO54hPafQeZMZIOhqgjhFKGGfsWURREudzXQJ2gx0H7o7tmPSaonXogTnu2sUTrH9/W4xIMvmP3o9BOoKGDJWFjXO5u6gyWmqqNf+SoEFGr9f1G7qYP/X0aSBL6PqoS3UirOeWvOxL+3qMAvAPmG9hUJ95KLNo9PQs4R9usOSSPyYGJ1+fS3WuBXXy7oD9wlf80GYXL/wiVuU+6VizxMvIiqRLC/K+GW+Tm5++KjhzhIs87EjsUUCYHvoBwmR/Ss0fVWq2fD4xu0mgWqqJHofiY4LiJcXWwK13yVgaXetCA+pUZ4JMP3HolrHSuaL4CtjoJoSaYeM8iAp3+CzXjBtR/V1GuJYHgLONEehXAo0L0Z7ipFv0cQbGB5ss8HbLlaampla0EOd0PVirgF5KFO19SOb3LQPj0LVJPVspkWGYvy0V3Hn6Uo1EF8qdH6yktcNhXLF7ehv0Td+Pjy6uv9A79XK/OoEI5lvDXWX3JWgfEBeXufeZc30Lh+Mz794HJD5Z8nlaMRWPSofNMzOTN1q7pBk/+0iHyi5dwO/t1dtVRlW/FpAkfOIiRmVRefrqraRaD8bqBeFNRmA3G9WwLwJir5pkIrcSnQcKbqqnx3gC+Z3rjJCW4brbBeyiqREhohWznSSNWWAcTH8nQEJB/6+XU0f9mHG/Wir4tDXDMOSf2hcWBzz8F286Jy+VFuaqSQ8jpGk61Tv3YpwjyhLIv07YIwol+LYnIt0cQHH0ySeU/ymi3tZGI3394S2+LioZtoAqf+ZvOfFogrfUA25e+Dmxz6PPEtIvpNpmP5Rbh6KGooommdujSBeve6Z+Bk+0l3t9TxXP0ZhcsE7ov7XzktJ1VZnc+HGQeDLp/XtuOV6VFL/0ZVmz+bnOm3MzcEXRtVy3ywg+TOsOdzJNwXIP34t2Rz8mwNQgvfH8L6l577TEbiaqYVMcr5jCvkgDiF/JUxXqFRFyfWlxVOKd8AR1PzZXLh9GSYbv85uxAjSSIuBp5H91dbBdE8c+BHchfZhJDSIxBX/Izx9P5vmxABTH85F6Trx9MlKUlT9JYczm0kZGY7l3eI99njj4ePOnkfuKumHU64rSe38nQ6awbKOUPIRyBo9H7eOgZ7h3lqbHEdMaO0OmDovXgw+4y00/NyNAM8j4nScWztlGP4XySzuV7d8BmiUg8eC5syRR+xA9O3hILausetAebfAgg8b1Z5r4Rl4Rmqv/BEje486MkXFoCMzj3Atkl4Bnvnl+fE0Y3ggk2HTd3a/J4VC9LWH1Kyi5NcH4suJeojPa5sCvmZNjNeWudaUxG+Lnnc2N45msq4lVfkk2lWpurctyNLyaCLUrYiG80HmxBZaU1Ukq68ideUgdzqmQsR9uRfIWPSr2+zCQIgZppr8l5u0QyCLte0CwyxKvfAt8jLBmeEr7brNE8YDOTX0hCaGeyogOWEIbstMTcAIsPEbmTGCOT/GOphHTIy6ZMn5gESI8L8Zhz3eh5GNaQlNoMv10QLVK1zMmE9ujYoriuNA8YvVsKDynHxvZqQ+lJPAJsIVhTTrpCqKURzjlAVRYhNlrkeWo5WOX6ER12yXq5EslGSRHWIeOGfb2ZC9seY3r7RqENN3dECpDAMHF5qrSGzRpr6S8fjei2gRkp00sDbt+euGC6Lrh17LHd/O2owW/ffL03Lgo2Ys67iQOUHKh4eM6qp/A602COeFGluqN6u178MR0pS62bwtMfXSyAuD/LDx8kfcDqnUwGWrYidTH87DxnwNCgtVmvNpGNmw8wgLFEZP69iXa6wyAnuixC82VM3olJVPHyZrLzZSP3yuDHOcCk2z1t+0AvOzROA7uSf1Xfug8KTxEB04yMu6qJIC4cirPZ5nDjU0W/s3FPaA7Wp/EvCEV5k8Vs/s5GV3namw42LkpCfEU28osCuonZKGspdvOoKzQpFuIRN4NcWIiLvemOTyHfIFabTSJga/G/JTMaeRP6z/SOR8J0MpCM64NAVm+K2bTk65fJLh5aaX4dguKe1NKf3Ih3CHdzSMUJRq85rbC6tUYPfDM1PB+dz6vzdLIk8cfhuiPg2xFlRtAkeHZVl0vXdgrv40knPioQXz7iS6lDgii3X4Zm2sPNe+Y2825UGyK4BKKHm3YmuCEEoM0qcf2cJGkLcAoQD/uTqb9nbJNvLvg79ITMvH/GJBj6hxblA80u/c3Nk77rRsMpsZRaJ2LCmVcnMaLd/L9IgHuMDYjqNDzyxsHtvykilY7cwFz072U8PEWenM9+Zd5UPAjamZ3L1cmO6TzifG27/eGNhRaut+P5o+K6mrSmpxp6L6KlgU25/XQ5GiEqj3dzm2Ew+jIvKolJHSuLxoyTIA5EFbdpRp/PIyaqwyZzcmSzKBrrRUPbXKTS0h4rnKMc+q6AlQ/UL/RScYuyNJ8REkQyoRaJUcgKTF9A5GK/XSGWiF33axg2eFKC67sKKG3plsxX7P3gEAkXch9Gz1C1QiD40E0EhP/FPzeVYKbkFRxryuxccvVqdkH2OCqJBL9GLm0rkoaKOUmV9szZmuEP8zW57+gg0d4CUw8vRzj2a5tNzsembRSPALsKHgk2VxoQe6LOygctY6/10FF7V5Twiabfh9p4wnm5QJv11h3V3VpEPcR/4pNhRD31x3pUgQtKipCKisNUxj0m5MhMUvDyQaqDjpvUapIqhKnCC6uuONSneQa4KfN18cqRJkMT4uhDCK5lkXMpsekGArcEKaPfnT9qGiXKD6bn4M+pAIkXbdcXxFzcpGbBdvpIrY8+HlYBXjA9+3zxzmvgFxylRkFaChK+nLHPNwkRVSmWPNI/G3cXeualq+/EW4vLjiuD+JUycKw9HsNfDMQKP0c+72Ccy+EXdDsJXXwPeBmMWR5uvOcTQ1JVoIGrr7J37eJgaqJeq6htVqJawgQSzKQl0K7KrbfFdvJcPZwctunCcfKxIlKM/CeLf92StPBQ3n+0yi84u6qE7R885Fuh4FwhDoS5Rx2I69SY6cY5HDueEuWRkca1Ag5JQTYoFme2Xex3yr4jWhxLU2FvlxqaVMEiJy5iyPHj5ykrkNW8ueYjg7AWAEXbCjwN+7KDPQBwJLPYrbMBBwE1YTN1TiBlOcfLSbP/8dZtSvdqn0SlhY/bej5OTnEuZHCXdJkoWIbZkH/pkbp1RoCjY+3p37D6hihrRlq/Gsd0WBaL1Ei8jmgbQ4WJH8jCDh36XOUcPkXQdVWWIepRKF1DGy+BbQN36SHQQ+ARevnOvr3TwzoTqr8GUguyXoOxMmnRM4ZlFNAUNVzp9FW0lU51W2IkoGKrA5NaMbsUtL+2PnaJ30AnoWQq6KS4nPT6N73LNqZIYYOY9SG6+ZWdb1ymR+6sdu+Du6bVv8JOJMKZPyamMDYNJZMvZ7+ELxuFBrnWxnyAEJUt4+QknwAxBPIAFXxU/iLh6l+UzAJ1INfVdnw6WXwJAF7gffbCr6pfK7OJVt+jF7CciCRapPuJYMusD8Igx7styFCW/3sBgs/xHNtInv6vjgXwAdfn769/RMJAGGq+uy1HjUOEw4u8ycPuPbzBpmWDSYy++FMsQ62cbBTsaRAPaYpwQbUbWDY2LSJa0gyVz24AvzRWnfvd+fNF53LNemL7SxYz98nUuotD00sQYYZ8GL9f5YXiBg6ciwGaUPTqUg7YiH9HuSuWePigZ13d7GTzInLZImRWJE/JSnMn5VOhgoNURMpNW9FEbFZEqs989lXA3FR2KIB5pCB97b/1mw2r3tMOcSnCr7miFQDN/snUbF1pG50Cl2aLrXQXdcfQKtGh+eBA1AjYv8UlnbKPXCI1B4L9oJIAlhKRXmmxOj0SlKvYZ1QmEV5VTqpeUmfhnxK//AO3GiT5rb7+HaJQ1+dZqS66lSJfHJSnsqq+rWjJoPZwhHIG67Dro2enPlqZkFpX8Hpl/IIOjUQD8t1CeMt5BVMDW/mcFXvollPz60s+WPpfOEu4BVB0Ck+3+rfcizxrbUt3k/aN1eHpjRfN0jrXQqYGPIZfDmfYuGOHL074YJ++Pr7HheAJqzUUZdaxsoHLPNIfrUcKL9mvG8xyM+nvQq/ZOI3DX/WcvVwBUgBAHRFfVyYJh3FnDWdtMvurfiZva8RA1zxFOtvMZiPp5yHmpspSaFiWpLBF3KbgLeoMnGynZUwdMadrgJdHmz2aH1lMgL6sNzPC0xsQnUzjnp0FUcujI/nAi9ZHPdn4pAsacvsC9yinQN8lK9gWEEo94V5fwF/rMMrD3yrtoV6MxDcgrsJa6RSVmKm5nOhlULrnrlyYzS5XERRhOHO0POn7KYtWGLAigs8R5qp6p8/K9SqieK7alBCkSxD0ibF9h1yY6yEj3VHIdfBSBiyPzJm94clRgAN6IdzoeYeqwe/J4kssslBO+wUx1jH7mUjVQl0sMG2WiQOQnIF+pRT2N35hBkMb2Tu/FDG4wFaH/Zas2MXhS2Qinn4WAb1U+t9zUMMN6ma+LrwWO4VCpqowgKdKluVvBSJoTf1/si09oqHxYpixuZcwJ/AEUM+Se5OqymcjzuyhONBa8UOQnwfcaFXiaITdlhBYSQvfZI//zwBfWD+VC7sGlCvfOb7BgfYgjextpthBwVrKxf696SCFAxNu8HwS5tmdG5V08LyUrLO5nBn0VsA5QjVz4MEE=\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" missing","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"LElKq9fY8CyU9HssDOv33EsHiFJrUknrBQonL6f33JXXDPb1TUnwO+uhz+quQp+kdfc4tm4gQRMxLoG7k8tHFAtj/17/bnj45NO8JAGgtUneVlpSpCQopg4Awtn8FC5hP52gqkFuIHosXKicHP3eyGx8bX7IdRj37bQouPqOtPpORVozRhJD469V4b1/cTFpss5gEMB/zRShsBrcMS/ooPtEkZnk8eMxrmgpQD/SzX2LkULVrMkr9EKvjwLhaxxYHngnkA5d40MPu01dsafCGymOOwTyDuVo40N3rs9LmowVAR08RR+OjnaqKv+yoQtAi6Dj2vyXMDWF0OBXsqDo2MDAsb4OEETRN+IaKFNPoRc55WxwxL1SL0Wucvd2dsSAB70pZmPp7qMy9VP8oLCR1S21RwrJxeHQdgw/7bDfJNijr1NVzkDrBhI1K5RpZl++Z1/fTP3GqH01ESV/XN88rXjmtZ6XJGhteRr/t5B66Qe4BC7oQKl+LvIeUUKFUX0nIJJkMmjuUXl2/uBvz0mdlipbCLxfNmbAkxge3sSsLZ9C8vtPx/lC1GL08TGu/BhEB1ru5VeuuTVlH+8fWfuhukr8iAE6wKlPjuyBMyp7vS6+wvU+Dadz/OBJIraI1G6mNV6ze12SIDgRQu5XAWdhdhLnkDc3lVFkCn09bMGyGwzkSRFpfcCj14iqZ+WW/RgA4FymCxQyVnKL7jl81LvKIVC9ChMBU8pt/jzD5gqe2CsE4wB9+dZUscXg1yAvZo/e27TdNYDSyUwiG589ja8plRtqGxImAIQruQh6pZ558RKSqAbw1KYsJqLiP7kxpu/FlTbqTzSRdpXiPeWSSJGTfrW3HqbDaRE2KY6T4Y43lEKB3tG+cQKgHx7Cl1V1fH7YieRcQoBbGQvuhATha9S0NQnztmcgaCKM88v9Sys7Z1KmteatGeHNZ/qwhMfUjWQPCAQedTskRIwd7UpqcqMq8L1vL2DYquRZ+z5urQiC6YISJtSBVqH7thMlQPnp6rao0Ffw6Si/qFtfZMo7lrDWHtKpUz+M/4HztXUr4hieN1gI7XtsTikDpQQVOYG5Xj+s0BoHElJk9WXZlilBB5JvQpoTjS1zyA2klMtQukSxnCIiLfN5PxkKl2LLYItfFnpfQ21c6x68adaXsxQw3/2UUNLghUEFFtg+LRpdI8nG5JF3d+lhFTpx0VVaqeGUhldGRvkq14KATbpn1opnnJ6a64IYOJP0Or23ao9qDY5jnnXexJUtimOeTWz0xT1y3Q4d0IiV8TLC6lkWnueRgnoO3n45JIQQHQpzn7pe9w9rzoMQvo3rlo8veNsbDTRmFiNm0GMOWwdGlR08qzxYl0EvNfavTGtXxYDqPf4N7GITN2jj+6BCk144gH+z1HlklevNkQqhsguHreqe19x3j7X6KlK4GdP5WQujHTiixnVZtB7mx+b4Hdka0C8fHDl6Py8zzkQTvYvs064yF7H7fCPlXrfuFMwczM8VGVa4ayElG37/tmQwnl0jICE6ZJLXJS1Rkbfg3SkPl1Re3vZiVSRCpjFcETjkBzsQzeUyLSHTHy8YvH4pkxyM84jVFco0heyJpOzlP0gI039n4NZQSWvG4xpsDT+VORtZN4hbZZQ1IrXiaeOKg0quEBw9gcYmywDDhfpnrIr8Iwne+FueTXD6vXCLCQ6FwufmpUa8V4jMARnobO6gC+/ABxGY/TcSVspozjBg/98mcZRsSkfMNIvKdqtIxeo+YnSNbsSHIGKBmngRusPrs6w4aackcY+gtapGI8vW7a6DSeigr3lB9sPkrk6CnonnT672s19T7veHQETWfb5O9Lp9CP2EG7qFjDIrd6N04k2OLNTCD5k4dwNxSG17IjU8FxSKT4Ffc/Q8jn4P+GFkj7+81+dWZuy0Cl/qJz3pliVxOuAxMU/4zmUL7zdaAFGhQnw5hqfKyVq5rjY12bUbf0oN9ZFv712j4KkxgMTKQ7hDw7ell9xCGCuA5KVpcVSfT4Rwzr5q1mgy0Jgxw4E//KMKjn9qqe/5sXbzIQkdbzy0wEXZBqbHKwtjAx+sxzWDtANGZdAu2QmE8BuhDQSBqot/R3QRzQzx5RuixrePld1lWflWgkaBwLvKcmP9UdnbXPckL2a2+/LFecgksCvVooN8zyiH1W5YXiRWVKH9TE5x2cI2X7SeMRshGUzZV9yfLR/RKMLTe329gsrWLsCcGjxzFpywHhlt7FAIWoiiRCtMU4arzj4kJjKz26/KKCGCEx1OgYD4GqY7IDKOriMOZuSw1XfXrhxKtU4ddfqXI+3gMxSS4Y03mc3yvonwJxzd1qXk0LEOwF6gj8xoXkpcyqoxNr8mSQNPhJeWhIFg1mlyyIRUVa05SIFKld1qc0AMk1n8/tCvzHpCbFScXL6o3o5b1EJetyGxdRmppr+AoovtQ03y0G2Z1uUR23AtbxkiZWlCB+g4Vx5eIm8e8uLw2s/AG6Qa8cPSKnhgJ2b9i3UKpVS2mHezLGTKHNsEVGxjKoAdLDrEaj+S6y10J3RdcQ5r70YuMAQKoT/NqYjiE825I2IvS7A7W6BIMFMuf2sPhCpT2RPtirK/ppC+Z8yGrW9cZFfGeHVTIetLNP4sMCc4R0ZBGcyBEv8gXFxRyQ1WTraIWQMJeMDGSfPwy8kwcammlgVyL53QGU8Yx90hXIiaUX+zbAmHjzk6T+yL/PrD3ed61A/eeyOwcWq0oQk30pvtk4VBeNw9vopFDJPJdoQayQREDTc6u4mGbj8luQlNtwtxaFzKTKjwm8ycMUxzGPyj38UMeuZePcJ3EAeMIAULi/3p0LHKnmJQliIeCMYZ1INd06BRUaH/TxlIICQveurvXIehKny9mq4DDMKStEkXVkMkCg25hlPVkEvHUB24FVjp5emIc2XCpJfN0jUzM59ZK0EepptE/CC5efI6I50BMs/ssxB/5mwDprSwVSHnHlcFZNgCp/ecTyouhS3BxwxfKHFNSEBkTayxz2i7U+GLA9jQ4C6a4oqf8Vq2KM7i7ShwtuBSZvgPWVWl6Vu5epxEatszjkdibEt4HtpcGaZuTdCYR6hUAWYn8IA2BqWS7gojfaJRV/jp0NDYXa9XvEvE9zLPHR3HBQLxEckUYyaoJk4uYeaUbs/ieR594MeMI7BJjaOieu3fXZeZozi+3lREEcDMCIvy7mBUaCBHZNcQFuqhRkRSA9X4CTy/QH0E9KfXkrAchjsQHB8X21Bql2GiWDZ+cthlVnRGmLLQG9/WwwYCrE3/q2T1aBnA6a0GUmH5OcDXdM4hI1NwkjW0hbY3NcleOh9Ad3LQ30Jw+pkEo+cPggLLwjKqUUGwpVM9N7OV8UqobRPP4yR2pi+57NKPBV+0iddQfClw1Uv6LubKBX02uRm/bGCtLqbrGTIlHDJ1aVIa9QoAon0fKfhh7nGAXxO6zIevC0c2UhttpiQEXO22i7orrmEaXYXTZSPKA8Z2BFlUlaHb2WDe/ox4IjQpQ07H+kaYT/Y72j0nrIJ1MYoBQfPToHd1hQG6wMCLZb0l+HwH/FxvlAYwpSj3u0V9D5wmLfVyadoLP4j7b+Xgd/VbR9DXrwsUskOgZEWOOs8CdayrparTSkQFVgYDDaMqgQkNqj1e/0kiXp+RrcFsrNz2r+icUeZ4DHbfrvTB8ENeQwjfpY7e8GmglGtllRAuSPxzuEwx6j6+Gn9hFehSfUNmO0ZPU7on1N1SRE0AqUZSjijRkVxXUkRAxmhg7GIjeP7faySGAGzViWCethRbzThYKDx5kXKjtbebq2wMbaAIamY/5XQsJmXndySfoATjUzR+Hz1ehCFvqWLuPQzr0pyjTindDIWDHwEiG5jn0zJ0NLcYZ9Bc2px+K1+VG+8vpFJPQkFnyLHCpMwNr4ZiOorfYQx6gbLy/AOSCcVEQJkWWLIh4TEWeuLDxYSaZydnwEfLjpjXgbrPi96ZlZcbyULIhTAqg6SFiXQr7W63dnJKzxrwA6QoV3TYQtGwRL2/03yk81+I8waZBFXNLjMABDY/KMc0VznMOU2HWIhHUpkUWcmitO+z2tdSp0K5CBtqFUeQzxHUllSuEj3J4ZLRresIbhf4gXB1eyIKPb0TbPbthVUDG3UVQQrChJ/Jz+E9mGRDQLtN3/I5IA7gqsqII1dzer78KAktdAKEt9S800p6puqQazddq0/Q8Y6ycVjJAEYshQunAjM0ev/TG4j4jjrZjjegNKfUvwKcHzYyNSLNSbqcNRzirL8Q1cGDANFsj4wl+lmggCD7p9G++uwtsWZ+iSkjzr8Q++A4zFwNB+WzxLlfSuQ12eXV0tEXTm1MW4rZ3IQO13DxP1s5m/TgjC7g5NlsdUrprBqvxSeK9ThHjRg+TKp2bYAiqaxd31bvbzu/r00QW9U9Tner9nXn7trZDnbfGRWRypXPCGTTH4u39vgO79x7mAglMMRV5txmFcgcgGAtPHF3Fri7C9vZfzNFAUBc8AWrAoVKrXlJ1bSelEu+j91ixD5OqZgP6upPQLTgASGArpU4AK40TUDQnSl4uy4nbTLN8RQKa5RJYvIvGXOiFao/1QYczOZWAS21BiEBcvmqfN7/MsjcUwjwMTgt6XfSGbGGukP+tusVeHuKA2XvbBULQfYZpIiL8fxrOKvYBTfhtRQB8IJdrYHj/+eMn0JbewYMQ+xrk2eOYqxTMtImotpuA8afG7Xu/0XkyCVBU1ZjFkpARSRYVtl1Se8RWMRzb7nyU0nmOV5GS0VdupQ8Qz02RijQWDtp/rAwlHpFBT6PfnnGM14N9oFpTc7wLrLXa4W5qlfpGeuinvmP54D7wqSr9G0ITNqFoilyPnQvDGCv2wnD8JN1koyOAT2O6SFrkdyTrD6NBVG82kOdPCBpDk5IYx0J5Xq0L8dIw596TwK1khVAcdBG4z0bISDg/ClFBhVrwHid3QX4kKJZA13V5B5tishx+6syMnkmowtM6w9jQRSxjaGFJu5fDC2SHxMHzsAI0bArNl01J8c3jZgCSfn/PoZy8fmBtMuYwsvRxRUFNmVKGfGnvcdApqVE9Fo8d9iHK/tM3UOSlmKQpeb5j7Komnx2O/By2RUzKvSCoJ2eKoRIDlcSQPJvRM9I8Hz74st96mmGwmhIqqkrrZ4Qfe+svK5mFcsQhRQg8vX3vNXifRa8Af/uuGksDfv+QtrLjGnZscmMp9H3lJmO++jyUH6Vy256tDjrSUjK2RBatgt1FJaPdYpQL/OfgzpMD4D980WvC4R3HIDpdtd2mRP2ZttmjBhAKwsbnAnX/+Axvg3eVnUdB8SDPmKLPibT2t44SW2WNZkIwhTH6CD0Wj2xzntFsBp9GJW7gAWWAymGClQQsKkbWFzFiTly6h4QW0Zf4se4yQQJymdfyAR0bzgkH9SM2OttGiNveotV+KcvanThl36/sNBm0MFSp0MDQvO/Gc5YMaZwvW6hwe32v8+c0WAMXrj/wnN2g8CrPPJ6P/GxN8rCIIov5TuTMD4yLeUb8t7BhEBPmL0YNWG41VQMGgsJTwzN50QNz4zkavfikQ37ngPt1OUH+TRVdXKrvwMQvD7O8u91uvCnU8YQzKQkQwqi+JFYJEFow5oVZlrPcJ76d16M5StLuKjCkZ8/EvN5+FcKgLuRK1m6klX1qKxLG1EjL3ORFbGzsECoHqwfzVsV+lWwQTVrvWmEZ5/CPiR3wPvPT4iNIiHwAoPCBip+AWBOmBYbyeMiMvC4GcJwgLAcU7cqag/JlnvnO45qXKtgptzYfilGUBLIdLzaYlsmwbXiC/8P7wof5rwwJnFaqnbcP/HpV7Q9sHMR8dGzw8r+n0hzK3v3wCvF5NO9eP5T8sdAz9ABO7TR1wTxBVvMqr6a64ylrs6KMwENKkMLQhpjJsh8F+qqX14ORJdUPE9R6w7NZKpp4VJnY3j+S/aI0wHl7c1dT20mrrKMl/QoqirKSponRv/aBnLPmm5qb8pRdC62wkDLsyz1EqJzML/Dloaqa1balI5Fkr1utkcqIyewK0xB6OSKlNvfq32bp9HjQSn362o0V1GZL8B8RMxy41bDpbNcARWGc6SrX/ODgEuy27wGnmTlLroJvqZaRm91CFvqY6/XcuJ/nCafDvE0dpNEoegEJXwJOj0e6DcqLyiH7nY21qElokBOTaQ9WvAonGKMry6mASll3//HegHO393Kpvhsh2oKXXOQ0omF3lm67pzB/P3qI6xsNQp4a0MBTyfda6yhHjxTzWYawqIJGK4xUCYAIS7sXrDChUmu7DDFWadWg1s9n8Y4fVzwGCpbXSpJ0j+BRrEVg3t+ZF/IZeJK31zBRUIOfaSTV+GjPBQyCRGsppF0KO9u0456X37azvIuZZvhV3l4dTvAFXyzjTkodJ1OyNCbCtqMx/zBDbOXgihXPqwGp8i+NI8vf3w1L6Klkj1Vg2UfwLO3tI2rbfHJLzv0/VZOcmkEsWKwQm262yFcCaLwtNFdZ/HJEiiHO+Sgy0KIh0PY322Ne3T0uuH653Z2RsvQSO9L8c9GMxkqpRc/FDKHoWLGiXCMOfteTuZjdKtB27Hu699ZjRfbBj5J/2VvkncJGSw7Upwa3pMxxN6UtW+LuR/DR/2079faUazpx0JFy0jsoZj54kKPiJHIuqXIK9u0wkqL1JvCaWkbINtZCOtreFxxf5t6yKEn7BftqTNpYXud64lf62b8nOwLjAOknqtgY8HQsmZQa0lZh1FVL0vHJgJxCmB48vbgxXZreEA1hrf2VNPAPTrQ82v+UDXTEKTC6uR4f7mQulhGknKH2r2q/h7jOUQ2QJgnqOfPmu69q9lsMpjcYQPQGVPcQAqThZt6K7tYKucLqQQKBMNwBgOHDDwPqmCKlOodEP0WYHZsjm1bdFEAWaMlRbQnI5iAZynXHmaGWX1I6E+f4hzkSIEQlLELp23qKl7cDA7Z507MSteCir9Q6IH8B/u4zG9MvRtAZS3qfiEhWKpLAl3fllEvsmDfDkHfkiL9fRQCCfMmKEzJCG82kDbr1BJ8BMJgga3yxMGNwz9u2HSyyV3elyUasblSMdQofiGWZJO8tI3RvfYjBMMTvzoiUBR1KgjjYD+arhjIw46NyYg9S6A2X+Bmg51AiHjhZFl21Fcz7LZLMp+1YToZoWT8CcfdmN15uIqg9zU3g2NWrnXViM92smDtsCiNKle8teB8pKXLn92tTkDza6I/+C27/GL9EtWnDykXt8XtHhGQO6y5fVWDo51hXM9UIuS+wx7NS6plD3YRvrRRq73wyBdtvdNWZzHPO9q1yW6HEsc+wBOLldwFJq9r0vOAoctztp8UiXB/cm30VEQk2iIjsngXJLbbDtKLM5dwwa6kWnY2NmdB0pBUlt8MyrkFiSUGnIelRFGxUTJkZ33AD+cmQyBxQxKg28RtFpssucCPe8xwB/iS6TtwFJZPl8gb0zpDJxbqkUETN5HaP0zRgJ9EkR70fhO61WcSKpqInCOFMRvq2g6Pmvimz9DoRvP9cMJT9gnKIAAmdOPiQLnMb03kc1ibjwHTcE8DkuuIWOUM8r2S2WLHfS9wbOMEs0Sr3/0X9jhEOV3yTK0wOMjUQdp5WF9hoLNqIaIfORA4zeOegFcv4BM007QZ1mmK89N63954c4JqfII5MXrgA+nd/uVx8/ySY4f891PlspSy7yr4A/uyfrD3zzzCjD8OHJEuICUGEu5QBuJQtQ+l+/LFGJieyIR6ED+DZkT23fT8aI/unkntg+u5dP8WV9rEl4dOtuVTZR12ub33RsBm/219PO5NC4kfamFT7w5f8+ibGiqgi7p0alZD453sWM2ErkFB5Ze4OS24RghTQ3NsEeA3Vse6V2sipfdEhH1+PrnE7lM6AkRsutBH60EQ+EUdii8+GmpCLFmg8eJYxH0ugDz8oNcgMqsrwhWEYMx0iUtuCWdJfclK5Sh9RXWIic6rSY2kwFzFWOxvltONdz7Pd7wh5vNx93sO1dj/SISsqk0+yYDvIf++/N9PxLZYeghS1+Yyxh+8ePG1Xw6XBnV2T3uqMGfVHJQzzTPZYGZy0H412A2RtvBLV7K8LHNB8XA7Pz4l1Dgk2dCBFlNgWdkx1wxnpaJ7oKgqdqDx6tBCEwmN35mH8lP3GoQWbibNToFLhX94GR0F+u3mpPs8yZejLckfbttrpka4a20xejYHkDvH2tsTrTPixhO4lYokvVf3C6njbq13nC3VKMa3eDdLZr3JK5VGkNkvvjdzBokrLXsoATQDfYNNvbEuKsdkrn8Z67/fyRqy++5TLhd0R0k7Kcs9UCgVCeSJ7HANBgAbe+iXZ/iJPFsXjzR0BkJOlh044qC9FLtmdJmGBoFgM4vvdQkw7fCSZzYwGCV6JjfsSuMvYjsOEzZ6D8aiHs8dNoY4UCt3f0lBrnFyu2fKAeVsN0j089D84xseWvrehusj9D6EpbEXOGHBY4fkaEtyp3GwEByV09AsKjTwwqwCPE2X37t6aTIq8xIaWG47Uq2K7ClIQISfMiQGx8GzAVINzcZTWkGYEfMlVPqUNBXlxCWnZlOMl7JwVZ25woJc+AKXQnbVhY8+HopdZKSP2DUDctVwlYLbSIDpQU6RZ2VeTO0vigkJWcHJt7eaD++3CHi3tyDfdMGBMxSa6nlNe/obJbT/wl/HzASsHVVxRVRNhh5NwkKJ1EefnhA2en6UPZJn1i6SYEOz647j8P/VhJbk+Z8ftQi0/1hGXnhXxZc8yfnVCysawL3l/Hm7sycFGalT19cxBvWHhuIDW+rjjyzxWWPn6P2HCvCDTyZuAUyTMaAYDOE6WbKqSpEPSg8ppgj2OtLM0N/hF4VtfMB6bCbU6R6o+2IVVItRaR6drZ8PbSiQmby1bvgpRdbiv9r8aWu3D2n41nKpj+5gaFklnAN+NOIgFIRDWTrjLEWK1+Ys9GDs9kKdPXEUJ3ME0+CAsU9YjYTizE95cNVQDncPsJh/xhg2mSLcyM1fDBlQhp8kmF0qiudVFAg8HHFEFOEDuC4i+AHTBG1297LEXZQmX2Gur2kjy7m1Rmgp0gKszs8//lTiB/A8SvtFFdphiOWEaTRHndhbGtu6GqtoTMp7vUN6KrdiUDVrNm19PGSiTu+s/E1ankxpU54zclwx5Fuo2OLxH9JDRz3qcUEWz8hdR+Rz5WHz4y+whS6FmHBLScUgFum0KJ4lpDBfsx/DGfoTqMQQDM/53OCu9fQHqUUi7zw6nTGWBJv4lR6/pVSHltS5vNEH82m78y1A/Zdj3W2HG1Afm3Jwuy3b8wXxMWTRhh6YEZSH0njXmjIjK34UOr72Zj7BEdBpqeV0IU2fIR7TOVYR0KeIGivUQFB9d/KYBcnInzp0ZYg6UwR+lY6Ji8Fxs0eE7yVWChtebpEv3CiTKn6PyN86kxI1lNYP0F97fTu7B9g4JmDDrKXJXdAGMEFa9IeTxznSiQO2KzYmppAGlS65hVMZnqb7Q8Lrio+sGmjgEiczvg5+uWtuC8jupxZ9CVwr7USm4tLx7DoKbt+563JNkqOcb/rzeKAniqjvfFY9doB4IGqI1Id3m8ycqbTXdHXDp1DazxqFMp4LLZys23JcrEng8nDn4Hhg0TShRTzN36OIhPdXchBeQD7asHBkNySehuXdT9WWjS3XozpSGnbm70FHpReynV3y2m+E8Tx3CgP3dELP1IpIjgpU/N4J65ZozKxoJBeM0hRMy5nchuzbLw6BzEtHUlaEOPY+rNweINc+V5g6+LFTErZWXXn3rR35pr7ar1cYL86xp+IDzuWEKyA9y57tghSU44LBewpclIE2nROU6l3OznIJKKC4/K9lVEvam8qsO6jjpUkFa/qjThzyHxDR7Wz9a7y/MHLMf33EMx4n/b/Ny42v0GRFUvG0rIUQLZ+nWwMh+zqBmoIBA3tSRJEMR7mJ/k/oLOo20CvJwRaMzghAjk7nyGlZOC5Hy8Dy04jWcA/C73bFQBVwR1lPgvYYXMpd/SWA/YxJzgjgcWT2nqZJg8r1x+TMB2JjuobvImVjUCzaMmGMyRzMKRvq856XzydEtAexZzOYse6r4weVfHuG87dvqidoF3QFt/P6KpaxQ/EkZnA8FDwjg8YrMy4axlN3TTUI5JIrDqij6R4cpIGzPSik/G9QvtUleC6l0r8xexhTO4QKCX1NMnnQVuOlOlf8nK2aDXTci8c56Q+NgNJO2cYK52WU1xW401iNmXOZX9ZNPtkrUIwXXyOD/Ms8TF4Oy5IyHI1e3jZ430RQWHcZAkroRzAGngeVtTfdhaiPyyvatNGrnGcvt51ctSqLxtzR1P3YSM5hXDeZCwvLfsbYBz0ooOx2EexVkn9P2YOXMQV5ie82mqEPjj39++EYhfnulH8yOQ9+WT8HzvhPG66Lk2znAZkPAKWG7i49kIyIAO0D0vCR0BQvegXwWw7gDt2yKnb9YMd8/HU78rKPkX37U7jWmL/kP1NoYx761GO1iSBYgN6P2yWOh8rIZQyYCgyuHZG7X1mpVuNEVfJLvhyOcO3/nmOP2ckKR7AbobA4FGdmcFkXHJOLFRTvagIESdYdtybJxPqdbshpqCicF2Fz/6jWTNP/G/pXoc4iNkzIOb82iznM/ZrF30Rhzed6qiYZozeIsiRS9/VxYF7iobX9L/N5Xa7p3YeWfTN9djUcGFCLNpqFGoctc8txuM+mGRDedy57kDiQRb29huICuPuhzhexiqq5vfZoslNRJzRF+ZrHSA1NACYLiWj9oYlDJmdyOU36mq9cUchDjWcgstnO32RwdYK8FCncqvARnpLinqIGGyKOxrFXM4VjkdK0O9Y3E3drRvjuoQjFjtXmEmQVu21t/BGdBaSDhaisYxswug3Ncg/O4d5guw19FPvKPkoHQyJlLtBJQhMblWcCVAvJjywo7Nxdv3CFRT52qJ8yOjJgppMatIwbgd3igAuBcZWrfRDUqyG9rRTaiAi89Z700bfRLT2pydmU+Rukr3qpyfmGqufRPtWGzTIv3qHn/j0lgQgUTX7K1TpL/bdCf5kQY6Eu3a28uUS8XRMyD8NmILwCqn5dJXjT7lIaftjTnR2FsKYJdln1lDL8KkZZNAJPArrw9tL1qoABK/dYw2O/GTe70Sc7lZ4noZvd2mfRhQMBwUiuF/HffDSFIxtHOe9/IIhhCfQL/sdF8bHphdf+mjhVdJkFOezSDlt40GHwevXkEQv3FK6txMgsDLV17/lPh9yccrLA9FTnDYnjEFwZJnc7JrVhekdU6CqLUyXBbiCh7gomJZn1hcTdFmy3J+JNmqyDWxcwyMadvVUaCcW7BJU4VtGDsWi0QDb/dWc5hNqFe93de6jhFwAHFz1ewtIkDcZNcjEEu2xBpvnys2lOPuRp22xqKaFOEcfCkIurw62X+/jiYJ6B/e3YjNVq2KMatU2VE42bI9dnWTFS/oDL6pkZqSN/NcH5esVMkMjzsE98FbPk5HH3R5eZbdh2xzxo4NF9qQMy5bsfj3F912kBVD2FwR3S9RPpixM8VHygJEYTuWoY7IhMkAD1vanMH0HGqhtkI/IneDQdXEzh7UE3pG71/Vo9pf1e4cgva/HdnevwnkN9iZJHwcaHCwsIm7eUTZC5gXR76fky762SWJrn/JsOmduLE2rSXQE7mnFEHzAg9LsPqYXlqGBQDMSyph1s6lhcbJtdKbucsHskif/4L4edaUkA+apoIjR0kEUuGJOOe+QDyIy2f71quIIKy9By4GktHIagcMn6GHX574/g2fqxMw76O7q88ZfDZbSN8/B60UjRb3cCari2fl+0/mFltVuB8hvWDcBoCsl55TgjfXgnG830h45pY2sVwcVPbfGq/8Y4ZPdHsUyHFqi1N5o7qNdcl5eUl5wXPFsqiYlfZbAAnL0FIUx4+E+uDBIofyGtdgWymk24r9zb7BnwUY3GuuA3jcrcNFPQtvSSmts75x9yDZecOFrTiWaHQrDw2fnQ8SrgLpD8+lnBEKmX82+osYAtaeXzJMrnwjd1LqUiNL+icPXKpjyWHN7zPYvW1YtobdR9REaEKckDTY9qurGiVE3b5CRQ9OrDrPLf/rlfCAgXpH1MxdSxmCsikJtv2jhSCrnTJbIKrt+GGDnYBqqthQf7X/trzamBBcDAJ5IAfwrlasoSPyKx1v6aO8uA0X/bitknXRPUTNP4xtQ34jFUJ7BDS4/i5hDLfygz9eAURyfqTMDKZSZr32g1PBfHWuv4sx1tGKuNqRpqMStNFbfSFZ2v/Y5HyVmyNTBlCh9STNGbDohxw/YF/6Hn5IGR4aPFIBoIHuuJI+WNVAhT75dWi6ojQYv7cGRB/ssPxkfjJ8JrTD3OGD08+ctXCU6524NFPVNUG7BWKUUnlOsP4mLawpIrfnF1ai3yp6Sybin4ynMF4jxd0+n+wUHCaA0p+rJhdjv0yfRCV5ry3fqBg2rkD2wGyh4Ge911UxarLTKnD5EagUYGNZ1X61vJAzBU0JbbxdEwtg1lr4qM+4L7KiWm76FzoEclrk7ZnTVEujJLXQDTLqnUIKNDm4Y4LOC9IEYXBBJ2q60ewCIdtDUHxJMAj053Ldrcf0DugFyk1C9ysTD9+JF1aLGmrU2hydlEzW/AT5nHs2zUU43+OcvVUtA2e0wACR0RpuL6z68xSCEWfjYJdF+YIw8R4Z+KkWat+HXj8yynza0wmJBIccFqhI9/uWzOzX+OQczMk8WU+UyutdE8/ISXYC1x2ZYcE0OXGy7eKMl5CYWFV5puI0P6uELgG6mbyzF2u8P6LCuEnsPCjKaHBtJQrnr1zYEQkZig8Ot6aiO75dea+tqCOj/VhVosW1V5irTznvrpZUEwD8/dXEd9m9xOursgOQuNKgHsaCi7uwlSXoPbJ97nyZBQPQuc8AZhFhjuv/CURh5XXe7IMyn+wgxxIOgUiQzeYJ+xISX0fbd+e3KJ659BC8V+gdy11b1F8RzfE38g2T/Sn7gRmu1oUAzAfO228aWEwIT3OrB7KMHjJP6xhs6AslTq6NUwc11mAE3DOFbgnry1xmg94qpDWnNnjQab7nECFiRnRpykpbAgLzC20dsHrtt2/Hgb0qOqRVT/y1sSzI5WchaXGGD1o5MKI6aEFdcG4/tq9AW0Y29ffb0WlylviPY30063R1x8bLF6A/Ac9Ve0mXTmIWAs4ZRNd1mRlBCQe9MgncTI8Ly7uvx3BJeVZlyzFHpXQw4PX2SzzJkX7lSJ2s9osvTDAF+jxFgEPRD0wmYWSZVlwl7MM5X2xkLF0X/xelH5y+3VrWKs7H23XsGBjwjd7PABO5CZTa7MuzVKOZA+wz+fZOzi0y0KufK5cr8vVcbKBULtZwn3kvDUFM8GycQp8/R7nAyYQ+S1BUyr5lIKC5ySCuMr/fay+VKm9OdG3Sz/jrmIf8f765YZTEhrd/G5Q3JnmSXV0t0RgLmb5iF1aw/Kvqb784i77+P15jn/2pj98wW4C3C3jLh3ZF6Pph9k8YAkSfBxOjPAL+lklvNH0DTuxPH3vc5VxTSvcLU8IAYNqDyRZE29np8kT2pm+yg5/aeo2t0g/eDBz2mJtRW8a7P4U2Pb5+eIDeeCuhh0MM1J0r6yXiGW+usUcKpEqWPiWhG1Wv5YIPeD4heoCj4W9OPb0IOupZJt0yzPrS2U/xtBzk/nmrT9iMila2yVWFro6+/4ZVjQFEnxjRwDZNcupJ9B6I87FPmq/v8MqqbNvH7SHyNPLQk6ijLuqvURqx9GcwD8yKYftXIXqN8jjM5H+ymsx1FzdUPpVdVx6NJSbLPoZcd3pfDelpsxqPtrrH6CCtI1td1aaqdoomCq9l1TSFHexIID+56oiyC+5tuR4DGPacydaUn5pWeM2KZcXhnkigQdnJbo8ayi4rw7cSOwQ5NAvOR4pH8Wdm9C5u8R8i1Dcg9ih1pvLhq70ooPqj2RycOlYBUdPNWyHmr62GkEqxeIvWS0MUCc2yp+TSq37Py2O+7vAbSHVJ964Hls0ZgpjKYVz3VIzqICOmwraPSetm5u5HG4Hudg0NWO93j7Sff8I9oU9Gh7FASYmNCCrN09oNy3YaQ4TFSA3JEeW5/XDvA3i54HdmtI4GxXA21QQsgWwPKvp3GP+EDO0f1yASUtix+TYGjLOD+Jj8kuVD1+qRaNqvSON/8qSRZZd4e4eiDwd4RcAIgrmqjqcnjJl6ikR2IuxhBHb36enbIoBB3ismPFf10tyPScZKd+xkqmT3IFR3Q8Nleft1LSS+61pTPVKN2MgkCe1cHvLjlGDgORUVsv/Dw0FYZ0QFN9CTs9HFn9bZ8YkBIPqf6DdkGTVvgZzelLQQpeETq7NhF2tlzn1tDmTLPDOBk/7k3xJSUARXBfmyxTk4P7HxT/dcy1Y2lT0mcwp2w2PHz7NoKuRWIYRKHqwXLtmNNxvx9b25CGqUQ9Ae+wQJx6xoQYFgIjfGkpHBsrEmYu/Xvekq0J62ojhIqp3waG7Q3Hq2+sX5eZ0goc8/6gZcbvjVgDXqzvE+THIAM4HiEHoXEtENl82/Bk/f2Dssnfmcv+mdZ4iXcUoqNk7ogp7EclJvFyEcqtG5Dsc5tcg8dAwdp2IT64LQw8zsSDf8aYnKGMl6AikFup9c4W8G4kJ+4UjU3fOpBuc4JlOc90VHTRJVhsHjFCg1XOSNNs+DWzdKfQtV/AelyK9kGXx93WodW2BHb2jX8rvOO7yQDVvcq11Z1PXkkLw4RiIB4WHZzGxRTBWxOE6SqYOYW14wcp5jjfXz32Cg1Fk1V6DVbsjAYAtHmlUtswMG2dco8x3/2ILGth9zrYI4I5YRtG/qIhG7n2LaKe1avbybdaY/23sHpTqTLYnH8LtS/91jlBgP4Z4u1LUd+VRnNh/3v1RuEJvr/SeySDm5I35f76qw64Svb/u6Zv9SMUz3IWvlu0H0P8SL3XC5KGq26OlFBdzuG1XUvdS/8G9d7rWp05f7ho7icYGpx9iVZIIWm9ryBr0oPUfJ46fK6JBGzPiYR6FJa9UQ3Rrv1Hjw9pHqAeyYaiv02dBHI3zwBszfWiM0RzD4i5fN05HkHHTY8hRQEfF80jQISLmzgMKA/EgQY4kmRZffUWUlsP0ZXD15ZNEQjtABkxBV5VzZpPYu7kK4D7meAXyhsRfJL+OAJ6aaDQiWHlYdyzuUePd7bIpL8wfXhwFfgXilSMCPC7bnHAfOVMEWAhivTbN32UFidkZ+2gwQduLGx0YcRO5JNXxE518POe4n4L5EtuwVnOE/LQpsA0Zw42ivNSjAt3/D3QIMRXc4UK7TDdseg8oX7igGcLkW12JMDudYTWCt4UDUtUJWqO2YnTxmZNpO96wzIIYx9cvDrjKkqU9TMb24fOeHWsk/kYamQZwPIRRtdFczgjdhUj3U/r2QAjUlB1DinTKTjqncgerYQbA6qTMNgD/vTe9plCSorm4l7Tj5s2/PqTXbks/0rlH38KweeMaadnlIxdOz6w4JYAbNmEU6vleYgzGFOI3wUJA9ebWQAYksNh3l4V2LY13qItW1jAQGK4yoRZOV/tus/bSWntJ+8+GNq+lxcYLpJlwsr60g8+3eqOiUU5K+jcgS/FZtcOA/Sx9mO+cMQjJqaBrf5HTNeewk5q7yJLU3fH5s/bnaCtBaeZoRld8QTaEw6FBYllelH0jfXPObwXmVIq2+d8f6gViZ2JIVctHOSanwjxo9a5OPCoqrP40T9gERIBqziTO4hZuBJI4UZ4uxn7Q6OGZTm5NmUqmc3SlJWW3NVZXa7rDl/x++lQ+oXvCxLN6Uj3hvmUxlBxG/6NMFChiSihS4jkAZ13BIxkCwmcgTKQNYu7qt7JeZPXUtjsYpkSAC9EWzlVI/kNSkNJVmLrBwjs6Rscrjt4MNUlUoWGpRj0RKzDdQRMjrKWQ6D7CNKjZVMEv2JbnySsMWYP72q0nSY0YUNKU/q2FS3vCYN00wKsnSx7y74fXnCoZe6vzLyyG2/QRjAv/1u1n/ppHU/AIhJz4/ZvRBtR03yHH4digOMcr25O32L5mGaAyfb6VDdr8eSRw2hJLMqIPG8l+d/Si8J9eHDGAXftfbWoWaJxJ9wDEDQgeAlRvewK5yzi/1lXu4tX/02oMd8CTgFjnoJnB0Yq87HE6NLLWk/cjAs33PQPMudfJ7A2sfcgeVVK/4dHcd3hGP2ROUdiF5LfaBqDxDbJj1ledaowW7jCJRyd1R5xBMPHKEmdM9he60nb5p+dstNqOgCM6UXsxZM6yYw97ZJrSLJRwpdmOKoJ3IrksmVzKTxutdUackfRdcxFvl8U1E5KSSGpszHGFv504ZwN6yNA9IfkF3N3zrtET/Gc1ahdsBW1rGzwaE5pPzXUrXu6w4nvnCADScYDMGieujkpAmkTVhYZP4nGgLJrZ8e8iN30455LpgTYVLLffIgJHqhfyooYa1oC6fz3aB7/tgkcH7E3iyR0OenKw8yvHWugAP2evwbb9bpoa3N1XvPopcUyzhdghUvZRLtEdTpnPL6zYChl61oHDSVqJOpre6gcCdKMqEBcE/qI9N0nrYc91+kL2Y0VVfvZc/lqNOsh41SgPsJ8L0pfYG6nunShs/1b8OBnYdVwX6yE/4W+QGIuT+2mTuqnHGpsvV7V+uw9SDbSV/3W1BpVpjk3NzVSa5ggwi+Bl2ApVrvLl0iGM1ECVjkIX/RjDkFKQwOdzoFK3OUACLMTfzGBwNQB2ozoBTD+dK/SEGg1a3d1zgl/BdK50iFrBKvbKd9hmeVkbcKGPOfyG0hP9t9KhB8904w7kFLtqT3XiVwJwz10le6dTcnlxX6My+gkl1Vv/3olKORfxBb0oB5C0gOCvPohPMEvMSNknvTNKBRdmKCpgQ8oM0iOkFeOxKtfdlZVXPJW5JU5D4UaDmPSMzmqYFhZ/SJFrg5GpIbNRHFI/YozaT54qzFSzwD/A6+2bIPY2v3HAasz/RUqJ4CWouhFtqY9RBMkosQX7NFVG+rYzqdWhxh6/g1PgN2njn0UbmHDmBQiqidakQT/fKKIkuYhqw9f48lrKfbqqixmBOu5uFK8ZJE8tqGqmIsc4UI22h1p+pAisP0Bep5dka9HE9bX0X7A1WVer5dBb+mFz1bLXH1rR+2W7t0/r0KeBQ1XRZEO/c7jvsRP63NJBbRg8IKQI5AxdUVlslnMbzCfIGaooWRaNyh16Wsc4QRPWzXfiKbl4Y+lkWLsz1TbI+flLy6lB3iFZA4NMTTzkhY1DamInbcqz08OkyEeLASwc3UyafTudnBGmnBXutqylDYAapDO0myioDkjjHYeQ5sJx37HTfy2RT7ddwSer8vmMzk59s2o4Rtb68bAz93iuzh66SBF/9kCs1rjP9gxHxGW5cfXSaZCTMCERdHtiOTKb2EC0fPROEMjp0qvU0yPb2MOSzGW6FF4wAvZk74vrvukBb+v+Megidk0LSIndzocANPf+ADrHPB0Z4K6/9rCMc3tyNBEKuHI7Y5CYbH91WOY8/tJWTYdm4p3I8ISliDKOSvUN6YmPR0DEegwcggD1OCMSYdMwHzeY0cjglwYKyZdBlb7XDILilyB1HyDQ5JiZRs2Dh5FKBMzRQlQVBM/MFwgkbaS/82hgEfJyhEGFRmGgY0+9FdJYlC4ITM7GUAdkYxRR62Sw1aGYUDDWPvTFgUERbMygDUXcG3hAH+ro+hwJKE0Mre7cZ0FWqEztWjrtQFdioiLxdQDdBSzf+6utsFCsbxLKll9LR60z7cxrkA2CRPVzzmLZU3APCLO0mmucUXd1mwc2EDE1JvAHs6n7x5HTK0gbKyJs4jCRBj9wP7weeiii2G3zfR1XWX/RF2juekt3In6SOkUoX3sZCnMQyjLteUfqsHSq0mT1HSA35EmGQYv7Dxu7BhgSFN6JGAtncjd1c63zmfps9ZCEeSUWVLrSSzlyjrSPtop5B0ZnB1tXJr+lJNbE8tWM6pvnjwkO06710goU2T2a9JDOxdVd/mmEiRf1S6tF9bRPwS5kKBCwP9M6jfy3xUpNmzIE6bFZ4qB6a1ZADYw5YNz9s9r0OndA+f5BZUlMmWjNqqmduJO7zgZ+Ssd3YmoIdnoMHlClqA4827xPlCWMBNGV0t/V/7dBvQX4u1Gnva0G8AzWRWkHv1ZtNTT23kd5+38F6ejfYkZLYK+ZDVKs1bgI+saYxD+FmkEyecwrho7cdzwUfv/K9K9H+KrQQ8251bs2fBj70HbNXMRzpfAgCVGTEMYgaz4vMFCFtrXuf/wW1KneIIi2X5/4Xwkm0L3q5bRrjbsmPcCjLvvaWEo9jBu9X56CFNM8INcWcFMs3zqr02cnqRdWivibb8eSzOzxNlJbUy6XrAG2TC92/n1Po7lYaAEKNovoym/hPtoXUUsFPJ9SsQs8L7T2dxIAt3cUnIp3UH5K6yJrpie9CtNNPWaG0j5A8ZUmJdLJll0wzWl/qcIl2dGeeY4ymLHt47QNwWRX1V6i2T9NERPerHX98NqC2EFhbuTAbOZXMlVd2IoYvtSDQivL+9e60TFqhyVDAFIC2d2rjrVY5dAQPpdBOfgejtu7AhgS9iqKvXZpgzKdrSgEaxI96msCIS1u1u2mgF3zuT9toAAh173x5sL3isJ1N/tTfg26yoDB7jx8FGLcVa2VTn6qAElbydgfAQtQtV+hZJOPgWBSAXUWRiUzHM4qGNXg6DmbTsKXdBbXOdo/6NCV0K2LXxarCCMH0WuzpIRElB0ctcU1mGGoF3N0Ef90kTqySc756iw1gcUk8W3nljADS6GrDRGzmtb1qAR5oJlxv3anJZ2pqfARIng7+UuxXKIqtPBfA/vUnAwUwZLoO3cLOSK2wrj+Zp0wAYnqwb2qAX3lyKKXsAZNheoF371IyDHbHz5VL8yPMvBwhODW5gUw0u/7VEPBu6QJlUSKsfXXuvO6IIcZ/xNXhtWRA1CM2RZE2edOXjOnp7ivDcBzRRh/QI0nknVlfBsLZEROH7IUkupYlp33t1EITNQT2Zpz36BQu1/CiCVC1olxi5ts3vliWYN+7OZFR/+xqCZ4xe10woZpBtHa6B9IXhjHT9pEWU84RYXfgSa6iHu6P16z8mwo7Ont2YxJ1CGmzgnu05lGHV+NMR9YU0uTEO8EUZu0B3GbOBVQay8hj+o2RTEOvhxEE9zqKYFrTLZIqdWwne3nEXYYuR4FGBWdDG04CgZ5vPzPhMXFQmOPDohkF1ny9KXBD61VpISWniI5ooO6MsRGBpJUmQbP9fl2pI5V0Mr1oEP6YavMdOLTXtWtj+EJOUkYrijTtW+Dgx+QPKp5Y7Z7lAbIrHe/SZyfe+VwN7X1VnvKCbf+mYskH9WtikPACr3W3kAMVZbEhxQYbCIcFi1G3ryQBdFbFZTD0PLqSosqR2zz9J+LvKJWnGq6nSPZ+yk795eJAz3WD2hdELDplgd3p9qcQAQ3oXWZR+GyS9FP3zpM/l83kcEDvXK3E0NgcfD5dbViTsFQxToUhr1psHQjGdFd2CeUqWFubwRzpOS3jg8MKnu1l2INGIzSvv4x9dodjQR1v9SikTiutBMm10a82eeDBU8ML0xwPxgePCwuYGpkGiHv92wcer2ER2l82EAdVhRLftalMXRpvoHD8wPYcwt3Swk8U+CgGAPJISodqcQUlvCMMeE5GVSFGs21rh7Q/k8AeqQuKGvDdOixfT9CmhWh/gv4f9dsT3jnX0DjKeRL6Mo10ndmzjgjA85kl5zY2Al8mn8TC2vQsJFlIPpMiooaDT3MLLCXMMKZIBkn8qX0mZw2MgEj0noM45c/4w4pkwpfzyLc2Iu8T2CsHD9nSK0+3Pk6ei4BoJEbh2c7TdjQqcCF3TOiE5sUDeEOmOK4ArQx2QOQ1mhVP+XXHQ4/4DgHz8QFMK0kpsba2o5Zob9AobYJhnwjGRXM+vpR0+aA1vvzzqUvdVoM+uiR5H8/nTV7Kh1voPmsRlT8SolOIQZ/p3zLO8PXnrmhwMHJP4TM5Ad2S8BlYq50/4fdijRBRby11w3k8r7LbM8TguSMYpuTsqs8448YVT5uFZ0EyFdBO/NX5R1FzEBS13uuyaKqkMHbuyu+umE5Yo5OWIemUQ7MjI+oZBEai1HGNHorAobDgJyDK0ByCR7M0DHaDKdFGYT3qf0P+6rtP0lv2NEefX9S63dOTNDvBJXzR9hvUGs0GpohCzec+W+YQ85gYs+VMf7sYcssyO1ss8WO27jFTK+etJmGa6UmGxTpjpka7YWTo0CvF3JkKqLutjchxMD+XuShjq9IsHAgaguyBRnVUQ1D2fMDJrP5kQKY5Jm/opFS0E1KbAaLv0dm0GwsxVrLblZM/YL8W/I/AIrIdJ/yEwr1rfO1WZNwaNG9X9/40bOfJG/euUA8jY9kuODxsvq3TgLoGyimRaqMpzbYoP/wCjU1FJ4wkJoGgLfhloYXJ1mtnclMrP1kxxMAVtj5NT190HLlt7IHWlQ1jiDr2Poj7XuzVgzt9BKs665MewJAmEpJbKsJcUPZ/XOPGCNc8LiaUnvZg9yAf9jLRUXZ7nmQjb+sSYY4RiygCKL2mMY7mBnJzlQG+PcW21GmkUrW4RQpm1c7I45cot/vXwnsHKL0isPfKoDb1dOEMi6XfU6GkQHi/Qsyz2179f22rH0gAS3t8ePJIEXkxXzCmJotdlpbZ9j5WnxkUXTq78cKL/JBsQkQ77CAbe+X9RiMIb9a2ieleA00Hgkj0Ep6NCiSO54hPafQeZMZIOhqgjhFKGGfsWURREudzXQJ2gx0H7o7tmPSaonXogTnu2sUTrH9/W4xIMvmP3o9BOoKGDJWFjXO5u6gyWmqqNf+SoEFGr9f1G7qYP/X0aSBL6PqoS3UirOeWvOxL+3qMAvAPmG9hUJ95KLNo9PQs4R9usOSSPyYGJ1+fS3WuBXXy7oD9wlf80GYXL/wiVuU+6VizxMvIiqRLC/K+GW+Tm5++KjhzhIs87EjsUUCYHvoBwmR/Ss0fVWq2fD4xu0mgWqqJHofiY4LiJcXWwK13yVgaXetCA+pUZ4JMP3HolrHSuaL4CtjoJoSaYeM8iAp3+CzXjBtR/V1GuJYHgLONEehXAo0L0Z7ipFv0cQbGB5ss8HbLlaampla0EOd0PVirgF5KFO19SOb3LQPj0LVJPVspkWGYvy0V3Hn6Uo1EF8qdH6yktcNhXLF7ehv0Td+Pjy6uv9A79XK/OoEI5lvDXWX3JWgfEBeXufeZc30Lh+Mz794HJD5Z8nlaMRWPSofNMzOTN1q7pBk/+0iHyi5dwO/t1dtVRlW/FpAkfOIiRmVRefrqraRaD8bqBeFNRmA3G9WwLwJir5pkIrcSnQcKbqqnx3gC+Z3rjJCW4brbBeyiqREhohWznSSNWWAcTH8nQEJB/6+XU0f9mHG/Wir4tDXDMOSf2hcWBzz8F286Jy+VFuaqSQ8jpGk61Tv3YpwjyhLIv07YIwol+LYnIt0cQHH0ySeU/ymi3tZGI3394S2+LioZtoAqf+ZvOfFogrfUA25e+Dmxz6PPEtIvpNpmP5Rbh6KGooommdujSBeve6Z+Bk+0l3t9TxXP0ZhcsE7ov7XzktJ1VZnc+HGQeDLp/XtuOV6VFL/0ZVmz+bnOm3MzcEXRtVy3ywg+TOsOdzJNwXIP34t2Rz8mwNQgvfH8L6l577TEbiaqYVMcr5jCvkgDiF/JUxXqFRFyfWlxVOKd8AR1PzZXLh9GSYbv85uxAjSSIuBp5H91dbBdE8c+BHchfZhJDSIxBX/Izx9P5vmxABTH85F6Trx9MlKUlT9JYczm0kZGY7l3eI99njj4ePOnkfuKumHU64rSe38nQ6awbKOUPIRyBo9H7eOgZ7h3lqbHEdMaO0OmDovXgw+4y00/NyNAM8j4nScWztlGP4XySzuV7d8BmiUg8eC5syRR+xA9O3hILausetAebfAgg8b1Z5r4Rl4Rmqv/BEje486MkXFoCMzj3Atkl4Bnvnl+fE0Y3ggk2HTd3a/J4VC9LWH1Kyi5NcH4suJeojPa5sCvmZNjNeWudaUxG+Lnnc2N45msq4lVfkk2lWpurctyNLyaCLUrYiG80HmxBZaU1Ukq68ideUgdzqmQsR9uRfIWPSr2+zCQIgZppr8l5u0QyCLte0CwyxKvfAt8jLBmeEr7brNE8YDOTX0hCaGeyogOWEIbstMTcAIsPEbmTGCOT/GOphHTIy6ZMn5gESI8L8Zhz3eh5GNaQlNoMv10QLVK1zMmE9ujYoriuNA8YvVsKDynHxvZqQ+lJPAJsIVhTTrpCqKURzjlAVRYhNlrkeWo5WOX6ER12yXq5EslGSRHWIeOGfb2ZC9seY3r7RqENN3dECpDAMHF5qrSGzRpr6S8fjei2gRkp00sDbt+euGC6Lrh17LHd/O2owW/ffL03Lgo2Ys67iQOUHKh4eM6qp/A602COeFGluqN6u178MR0pS62bwtMfXSyAuD/LDx8kfcDqnUwGWrYidTH87DxnwNCgtVmvNpGNmw8wgLFEZP69iXa6wyAnuixC82VM3olJVPHyZrLzZSP3yuDHOcCk2z1t+0AvOzROA7uSf1Xfug8KTxEB04yMu6qJIC4cirPZ5nDjU0W/s3FPaA7Wp/EvCEV5k8Vs/s5GV3namw42LkpCfEU28osCuonZKGspdvOoKzQpFuIRN4NcWIiLvemOTyHfIFabTSJga/G/JTMaeRP6z/SOR8J0MpCM64NAVm+K2bTk65fJLh5aaX4dguKe1NKf3Ih3CHdzSMUJRq85rbC6tUYPfDM1PB+dz6vzdLIk8cfhuiPg2xFlRtAkeHZVl0vXdgrv40knPioQXz7iS6lDgii3X4Zm2sPNe+Y2825UGyK4BKKHm3YmuCEEoM0qcf2cJGkLcAoQD/uTqb9nbJNvLvg79ITMvH/GJBj6hxblA80u/c3Nk77rRsMpsZRaJ2LCmVcnMaLd/L9IgHuMDYjqNDzyxsHtvykilY7cwFz072U8PEWenM9+Zd5UPAjamZ3L1cmO6TzifG27/eGNhRaut+P5o+K6mrSmpxp6L6KlgU25/XQ5GiEqj3dzm2Ew+jIvKolJHSuLxoyTIA5EFbdpRp/PIyaqwyZzcmSzKBrrRUPbXKTS0h4rnKMc+q6AlQ/UL/RScYuyNJ8REkQyoRaJUcgKTF9A5GK/XSGWiF33axg2eFKC67sKKG3plsxX7P3gEAkXch9Gz1C1QiD40E0EhP/FPzeVYKbkFRxryuxccvVqdkH2OCqJBL9GLm0rkoaKOUmV9szZmuEP8zW57+gg0d4CUw8vRzj2a5tNzsembRSPALsKHgk2VxoQe6LOygctY6/10FF7V5Twiabfh9p4wnm5QJv11h3V3VpEPcR/4pNhRD31x3pUgQtKipCKisNUxj0m5MhMUvDyQaqDjpvUapIqhKnCC6uuONSneQa4KfN18cqRJkMT4uhDCK5lkXMpsekGArcEKaPfnT9qGiXKD6bn4M+pAIkXbdcXxFzcpGbBdvpIrY8+HlYBXjA9+3zxzmvgFxylRkFaChK+nLHPNwkRVSmWPNI/G3cXeualq+/EW4vLjiuD+JUycKw9HsNfDMQKP0c+72Ccy+EXdDsJXXwPeBmMWR5uvOcTQ1JVoIGrr7J37eJgaqJeq6htVqJawgQSzKQl0K7KrbfFdvJcPZwctunCcfKxIlKM/CeLf92StPBQ3n+0yi84u6qE7R885Fuh4FwhDoS5Rx2I69SY6cY5HDueEuWRkca1Ag5JQTYoFme2Xex3yr4jWhxLU2FvlxqaVMEiJy5iyPHj5ykrkNW8ueYjg7AWAEXbCjwN+7KDPQBwJLPYrbMBBwE1YTN1TiBlOcfLSbP/8dZtSvdqn0SlhY/bej5OTnEuZHCXdJkoWIbZkH/pkbp1RoCjY+3p37D6hihrRlq/Gsd0WBaL1Ei8jmgbQ4WJH8jCDh36XOUcPkXQdVWWIepRKF1DGy+BbQN36SHQQ+ARevnOvr3TwzoTqr8GUguyXoOxMmnRM4ZlFNAUNVzp9FW0lU51W2IkoGKrA5NaMbsUtL+2PnaJ30AnoWQq6KS4nPT6N73LNqZIYYOY9SG6+ZWdb1ymR+6sdu+Du6bVv8JOJMKZPyamMDYNJZMvZ7+ELxuFBrnWxnyAEJUt4+QknwAxBPIAFXxU/iLh6l+UzAJ1INfVdnw6WXwJAF7gffbCr6pfK7OJVt+jF7CciCRapPuJYMusD8Igx7styFCW/3sBgs/xHNtInv6vjgXwAdfn769/RMJAGGq+uy1HjUOEw4u8ycPuPbzBpmWDSYy++FMsQ62cbBTsaRAPaYpwQbUbWDY2LSJa0gyVz24AvzRWnfvd+fNF53LNemL7SxYz98nUuotD00sQYYZ8GL9f5YXiBg6ciwGaUPTqUg7YiH9HuSuWePigZ13d7GTzInLZImRWJE/JSnMn5VOhgoNURMpNW9FEbFZEqs989lXA3FR2KIB5pCB97b/1mw2r3tMOcSnCr7miFQDN/snUbF1pG50Cl2aLrXQXdcfQKtGh+eBA1AjYv8UlnbKPXCI1B4L9oJIAlhKRXmmxOj0SlKvYZ1QmEV5VTqpeUmfhnxK//AO3GiT5rb7+HaJQ1+dZqS66lSJfHJSnsqq+rWjJoPZwhHIG67Dro2enPlqZkFpX8Hpl/IIOjUQD8t1CeMt5BVMDW/mcFXvollPz60s+WPpfOEu4BVB0Ck+3+rfcizxrbUt3k/aN1eHpjRfN0jrXQqYGPIZfDmfYuGOHL074YJ++Pr7HheAJqzUUZdaxsoHLPNIfrUcKL9mvG8xyM+nvQq/ZOI3DX/WcvVwBUgBAHRFfVyYJh3FnDWdtMvurfiZva8RA1zxFOtvMZiPp5yHmpspSaFiWpLBF3KbgLeoMnGynZUwdMadrgJdHmz2aH1lMgL6sNzPC0xsQnUzjnp0FUcujI/nAi9ZHPdn4pAsacvsC9yinQN8lK9gWEEo94V5fwF/rMMrD3yrtoV6MxDcgrsJa6RSVmKm5nOhlULrnrlyYzS5XERRhOHO0POn7KYtWGLAigs8R5qp6p8/K9SqieK7alBCkSxD0ibF9h1yY6yEj3VHIdfBSBiyPzJm94clRgAN6IdzoeYeqwe/J4kssslBO+wUx1jH7mUjVQl0sMG2WiQOQnIF+pRT2N35hBkMb2Tu/FDG4wFaH/Zas2MXhS2Qinn4WAb1U+t9zUMMN6ma+LrwWO4VCpqowgKdKluVvBSJoTf1/si09oqHxYpixuZcwJ/AEUM+Se5OqymcjzuyhONBa8UOQnwfcaFXiaITdlhBYSQvfZI//zwBfWD+VC7sGlCvfOb7BgfYgjextpthBwVrKxf696SCFAxNu8HwS5tmdG5V08LyUrLO5nBn0VsA5QjVz4MEE=\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":");","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" extract","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" consolidate","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" repeated","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"LElKq9fY8CyU9HssDOv33EsHiFJrUknrBQonL6f33JXXDPb1TUnwO+uhz+quQp+kdfc4tm4gQRMxLoG7k8tHFAtj/17/bnj45NO8JAGgtUneVlpSpCQopg4Awtn8FC5hP52gqkFuIHosXKicHP3eyGx8bX7IdRj37bQouPqOtPpORVozRhJD469V4b1/cTFpss5gEMB/zRShsBrcMS/ooPtEkZnk8eMxrmgpQD/SzX2LkULVrMkr9EKvjwLhaxxYHngnkA5d40MPu01dsafCGymOOwTyDuVo40N3rs9LmowVAR08RR+OjnaqKv+yoQtAi6Dj2vyXMDWF0OBXsqDo2MDAsb4OEETRN+IaKFNPoRc55WxwxL1SL0Wucvd2dsSAB70pZmPp7qMy9VP8oLCR1S21RwrJxeHQdgw/7bDfJNijr1NVzkDrBhI1K5RpZl++Z1/fTP3GqH01ESV/XN88rXjmtZ6XJGhteRr/t5B66Qe4BC7oQKl+LvIeUUKFUX0nIJJkMmjuUXl2/uBvz0mdlipbCLxfNmbAkxge3sSsLZ9C8vtPx/lC1GL08TGu/BhEB1ru5VeuuTVlH+8fWfuhukr8iAE6wKlPjuyBMyp7vS6+wvU+Dadz/OBJIraI1G6mNV6ze12SIDgRQu5XAWdhdhLnkDc3lVFkCn09bMGyGwzkSRFpfcCj14iqZ+WW/RgA4FymCxQyVnKL7jl81LvKIVC9ChMBU8pt/jzD5gqe2CsE4wB9+dZUscXg1yAvZo/e27TdNYDSyUwiG589ja8plRtqGxImAIQruQh6pZ558RKSqAbw1KYsJqLiP7kxpu/FlTbqTzSRdpXiPeWSSJGTfrW3HqbDaRE2KY6T4Y43lEKB3tG+cQKgHx7Cl1V1fH7YieRcQoBbGQvuhATha9S0NQnztmcgaCKM88v9Sys7Z1KmteatGeHNZ/qwhMfUjWQPCAQedTskRIwd7UpqcqMq8L1vL2DYquRZ+z5urQiC6YISJtSBVqH7thMlQPnp6rao0Ffw6Si/qFtfZMo7lrDWHtKpUz+M/4HztXUr4hieN1gI7XtsTikDpQQVOYG5Xj+s0BoHElJk9WXZlilBB5JvQpoTjS1zyA2klMtQukSxnCIiLfN5PxkKl2LLYItfFnpfQ21c6x68adaXsxQw3/2UUNLghUEFFtg+LRpdI8nG5JF3d+lhFTpx0VVaqeGUhldGRvkq14KATbpn1opnnJ6a64IYOJP0Or23ao9qDY5jnnXexJUtimOeTWz0xT1y3Q4d0IiV8TLC6lkWnueRgnoO3n45JIQQHQpzn7pe9w9rzoMQvo3rlo8veNsbDTRmFiNm0GMOWwdGlR08qzxYl0EvNfavTGtXxYDqPf4N7GITN2jj+6BCk144gH+z1HlklevNkQqhsguHreqe19x3j7X6KlK4GdP5WQujHTiixnVZtB7mx+b4Hdka0C8fHDl6Py8zzkQTvYvs064yF7H7fCPlXrfuFMwczM8VGVa4ayElG37/tmQwnl0jICE6ZJLXJS1Rkbfg3SkPl1Re3vZiVSRCpjFcETjkBzsQzeUyLSHTHy8YvH4pkxyM84jVFco0heyJpOzlP0gI039n4NZQSWvG4xpsDT+VORtZN4hbZZQ1IrXiaeOKg0quEBw9gcYmywDDhfpnrIr8Iwne+FueTXD6vXCLCQ6FwufmpUa8V4jMARnobO6gC+/ABxGY/TcSVspozjBg/98mcZRsSkfMNIvKdqtIxeo+YnSNbsSHIGKBmngRusPrs6w4aackcY+gtapGI8vW7a6DSeigr3lB9sPkrk6CnonnT672s19T7veHQETWfb5O9Lp9CP2EG7qFjDIrd6N04k2OLNTCD5k4dwNxSG17IjU8FxSKT4Ffc/Q8jn4P+GFkj7+81+dWZuy0Cl/qJz3pliVxOuAxMU/4zmUL7zdaAFGhQnw5hqfKyVq5rjY12bUbf0oN9ZFv712j4KkxgMTKQ7hDw7ell9xCGCuA5KVpcVSfT4Rwzr5q1mgy0Jgxw4E//KMKjn9qqe/5sXbzIQkdbzy0wEXZBqbHKwtjAx+sxzWDtANGZdAu2QmE8BuhDQSBqot/R3QRzQzx5RuixrePld1lWflWgkaBwLvKcmP9UdnbXPckL2a2+/LFecgksCvVooN8zyiH1W5YXiRWVKH9TE5x2cI2X7SeMRshGUzZV9yfLR/RKMLTe329gsrWLsCcGjxzFpywHhlt7FAIWoiiRCtMU4arzj4kJjKz26/KKCGCEx1OgYD4GqY7IDKOriMOZuSw1XfXrhxKtU4ddfqXI+3gMxSS4Y03mc3yvonwJxzd1qXk0LEOwF6gj8xoXkpcyqoxNr8mSQNPhJeWhIFg1mlyyIRUVa05SIFKld1qc0AMk1n8/tCvzHpCbFScXL6o3o5b1EJetyGxdRmppr+AoovtQ03y0G2Z1uUR23AtbxkiZWlCB+g4Vx5eIm8e8uLw2s/AG6Qa8cPSKnhgJ2b9i3UKpVS2mHezLGTKHNsEVGxjKoAdLDrEaj+S6y10J3RdcQ5r70YuMAQKoT/NqYjiE825I2IvS7A7W6BIMFMuf2sPhCpT2RPtirK/ppC+Z8yGrW9cZFfGeHVTIetLNP4sMCc4R0ZBGcyBEv8gXFxRyQ1WTraIWQMJeMDGSfPwy8kwcammlgVyL53QGU8Yx90hXIiaUX+zbAmHjzk6T+yL/PrD3ed61A/eeyOwcWq0oQk30pvtk4VBeNw9vopFDJPJdoQayQREDTc6u4mGbj8luQlNtwtxaFzKTKjwm8ycMUxzGPyj38UMeuZePcJ3EAeMIAULi/3p0LHKnmJQliIeCMYZ1INd06BRUaH/TxlIICQveurvXIehKny9mq4DDMKStEkXVkMkCg25hlPVkEvHUB24FVjp5emIc2XCpJfN0jUzM59ZK0EepptE/CC5efI6I50BMs/ssxB/5mwDprSwVSHnHlcFZNgCp/ecTyouhS3BxwxfKHFNSEBkTayxz2i7U+GLA9jQ4C6a4oqf8Vq2KM7i7ShwtuBSZvgPWVWl6Vu5epxEatszjkdibEt4HtpcGaZuTdCYR6hUAWYn8IA2BqWS7gojfaJRV/jp0NDYXa9XvEvE9zLPHR3HBQLxEckUYyaoJk4uYeaUbs/ieR594MeMI7BJjaOieu3fXZeZozi+3lREEcDMCIvy7mBUaCBHZNcQFuqhRkRSA9X4CTy/QH0E9KfXkrAchjsQHB8X21Bql2GiWDZ+cthlVnRGmLLQG9/WwwYCrE3/q2T1aBnA6a0GUmH5OcDXdM4hI1NwkjW0hbY3NcleOh9Ad3LQ30Jw+pkEo+cPggLLwjKqUUGwpVM9N7OV8UqobRPP4yR2pi+57NKPBV+0iddQfClw1Uv6LubKBX02uRm/bGCtLqbrGTIlHDJ1aVIa9QoAon0fKfhh7nGAXxO6zIevC0c2UhttpiQEXO22i7orrmEaXYXTZSPKA8Z2BFlUlaHb2WDe/ox4IjQpQ07H+kaYT/Y72j0nrIJ1MYoBQfPToHd1hQG6wMCLZb0l+HwH/FxvlAYwpSj3u0V9D5wmLfVyadoLP4j7b+Xgd/VbR9DXrwsUskOgZEWOOs8CdayrparTSkQFVgYDDaMqgQkNqj1e/0kiXp+RrcFsrNz2r+icUeZ4DHbfrvTB8ENeQwjfpY7e8GmglGtllRAuSPxzuEwx6j6+Gn9hFehSfUNmO0ZPU7on1N1SRE0AqUZSjijRkVxXUkRAxmhg7GIjeP7faySGAGzViWCethRbzThYKDx5kXKjtbebq2wMbaAIamY/5XQsJmXndySfoATjUzR+Hz1ehCFvqWLuPQzr0pyjTindDIWDHwEiG5jn0zJ0NLcYZ9Bc2px+K1+VG+8vpFJPQkFnyLHCpMwNr4ZiOorfYQx6gbLy/AOSCcVEQJkWWLIh4TEWeuLDxYSaZydnwEfLjpjXgbrPi96ZlZcbyULIhTAqg6SFiXQr7W63dnJKzxrwA6QoV3TYQtGwRL2/03yk81+I8waZBFXNLjMABDY/KMc0VznMOU2HWIhHUpkUWcmitO+z2tdSp0K5CBtqFUeQzxHUllSuEj3J4ZLRresIbhf4gXB1eyIKPb0TbPbthVUDG3UVQQrChJ/Jz+E9mGRDQLtN3/I5IA7gqsqII1dzer78KAktdAKEt9S800p6puqQazddq0/Q8Y6ycVjJAEYshQunAjM0ev/TG4j4jjrZjjegNKfUvwKcHzYyNSLNSbqcNRzirL8Q1cGDANFsj4wl+lmggCD7p9G++uwtsWZ+iSkjzr8Q++A4zFwNB+WzxLlfSuQ12eXV0tEXTm1MW4rZ3IQO13DxP1s5m/TgjC7g5NlsdUrprBqvxSeK9ThHjRg+TKp2bYAiqaxd31bvbzu/r00QW9U9Tner9nXn7trZDnbfGRWRypXPCGTTH4u39vgO79x7mAglMMRV5txmFcgcgGAtPHF3Fri7C9vZfzNFAUBc8AWrAoVKrXlJ1bSelEu+j91ixD5OqZgP6upPQLTgASGArpU4AK40TUDQnSl4uy4nbTLN8RQKa5RJYvIvGXOiFao/1QYczOZWAS21BiEBcvmqfN7/MsjcUwjwMTgt6XfSGbGGukP+tusVeHuKA2XvbBULQfYZpIiL8fxrOKvYBTfhtRQB8IJdrYHj/+eMn0JbewYMQ+xrk2eOYqxTMtImotpuA8afG7Xu/0XkyCVBU1ZjFkpARSRYVtl1Se8RWMRzb7nyU0nmOV5GS0VdupQ8Qz02RijQWDtp/rAwlHpFBT6PfnnGM14N9oFpTc7wLrLXa4W5qlfpGeuinvmP54D7wqSr9G0ITNqFoilyPnQvDGCv2wnD8JN1koyOAT2O6SFrkdyTrD6NBVG82kOdPCBpDk5IYx0J5Xq0L8dIw596TwK1khVAcdBG4z0bISDg/ClFBhVrwHid3QX4kKJZA13V5B5tishx+6syMnkmowtM6w9jQRSxjaGFJu5fDC2SHxMHzsAI0bArNl01J8c3jZgCSfn/PoZy8fmBtMuYwsvRxRUFNmVKGfGnvcdApqVE9Fo8d9iHK/tM3UOSlmKQpeb5j7Komnx2O/By2RUzKvSCoJ2eKoRIDlcSQPJvRM9I8Hz74st96mmGwmhIqqkrrZ4Qfe+svK5mFcsQhRQg8vX3vNXifRa8Af/uuGksDfv+QtrLjGnZscmMp9H3lJmO++jyUH6Vy256tDjrSUjK2RBatgt1FJaPdYpQL/OfgzpMD4D980WvC4R3HIDpdtd2mRP2ZttmjBhAKwsbnAnX/+Axvg3eVnUdB8SDPmKLPibT2t44SW2WNZkIwhTH6CD0Wj2xzntFsBp9GJW7gAWWAymGClQQsKkbWFzFiTly6h4QW0Zf4se4yQQJymdfyAR0bzgkH9SM2OttGiNveotV+KcvanThl36/sNBm0MFSp0MDQvO/Gc5YMaZwvW6hwe32v8+c0WAMXrj/wnN2g8CrPPJ6P/GxN8rCIIov5TuTMD4yLeUb8t7BhEBPmL0YNWG41VQMGgsJTwzN50QNz4zkavfikQ37ngPt1OUH+TRVdXKrvwMQvD7O8u91uvCnU8YQzKQkQwqi+JFYJEFow5oVZlrPcJ76d16M5StLuKjCkZ8/EvN5+FcKgLuRK1m6klX1qKxLG1EjL3ORFbGzsECoHqwfzVsV+lWwQTVrvWmEZ5/CPiR3wPvPT4iNIiHwAoPCBip+AWBOmBYbyeMiMvC4GcJwgLAcU7cqag/JlnvnO45qXKtgptzYfilGUBLIdLzaYlsmwbXiC/8P7wof5rwwJnFaqnbcP/HpV7Q9sHMR8dGzw8r+n0hzK3v3wCvF5NO9eP5T8sdAz9ABO7TR1wTxBVvMqr6a64ylrs6KMwENKkMLQhpjJsh8F+qqX14ORJdUPE9R6w7NZKpp4VJnY3j+S/aI0wHl7c1dT20mrrKMl/QoqirKSponRv/aBnLPmm5qb8pRdC62wkDLsyz1EqJzML/Dloaqa1balI5Fkr1utkcqIyewK0xB6OSKlNvfq32bp9HjQSn362o0V1GZL8B8RMxy41bDpbNcARWGc6SrX/ODgEuy27wGnmTlLroJvqZaRm91CFvqY6/XcuJ/nCafDvE0dpNEoegEJXwJOj0e6DcqLyiH7nY21qElokBOTaQ9WvAonGKMry6mASll3//HegHO393Kpvhsh2oKXXOQ0omF3lm67pzB/P3qI6xsNQp4a0MBTyfda6yhHjxTzWYawqIJGK4xUCYAIS7sXrDChUmu7DDFWadWg1s9n8Y4fVzwGCpbXSpJ0j+BRrEVg3t+ZF/IZeJK31zBRUIOfaSTV+GjPBQyCRGsppF0KO9u0456X37azvIuZZvhV3l4dTvAFXyzjTkodJ1OyNCbCtqMx/zBDbOXgihXPqwGp8i+NI8vf3w1L6Klkj1Vg2UfwLO3tI2rbfHJLzv0/VZOcmkEsWKwQm262yFcCaLwtNFdZ/HJEiiHO+Sgy0KIh0PY322Ne3T0uuH653Z2RsvQSO9L8c9GMxkqpRc/FDKHoWLGiXCMOfteTuZjdKtB27Hu699ZjRfbBj5J/2VvkncJGSw7Upwa3pMxxN6UtW+LuR/DR/2079faUazpx0JFy0jsoZj54kKPiJHIuqXIK9u0wkqL1JvCaWkbINtZCOtreFxxf5t6yKEn7BftqTNpYXud64lf62b8nOwLjAOknqtgY8HQsmZQa0lZh1FVL0vHJgJxCmB48vbgxXZreEA1hrf2VNPAPTrQ82v+UDXTEKTC6uR4f7mQulhGknKH2r2q/h7jOUQ2QJgnqOfPmu69q9lsMpjcYQPQGVPcQAqThZt6K7tYKucLqQQKBMNwBgOHDDwPqmCKlOodEP0WYHZsjm1bdFEAWaMlRbQnI5iAZynXHmaGWX1I6E+f4hzkSIEQlLELp23qKl7cDA7Z507MSteCir9Q6IH8B/u4zG9MvRtAZS3qfiEhWKpLAl3fllEvsmDfDkHfkiL9fRQCCfMmKEzJCG82kDbr1BJ8BMJgga3yxMGNwz9u2HSyyV3elyUasblSMdQofiGWZJO8tI3RvfYjBMMTvzoiUBR1KgjjYD+arhjIw46NyYg9S6A2X+Bmg51AiHjhZFl21Fcz7LZLMp+1YToZoWT8CcfdmN15uIqg9zU3g2NWrnXViM92smDtsCiNKle8teB8pKXLn92tTkDza6I/+C27/GL9EtWnDykXt8XtHhGQO6y5fVWDo51hXM9UIuS+wx7NS6plD3YRvrRRq73wyBdtvdNWZzHPO9q1yW6HEsc+wBOLldwFJq9r0vOAoctztp8UiXB/cm30VEQk2iIjsngXJLbbDtKLM5dwwa6kWnY2NmdB0pBUlt8MyrkFiSUGnIelRFGxUTJkZ33AD+cmQyBxQxKg28RtFpssucCPe8xwB/iS6TtwFJZPl8gb0zpDJxbqkUETN5HaP0zRgJ9EkR70fhO61WcSKpqInCOFMRvq2g6Pmvimz9DoRvP9cMJT9gnKIAAmdOPiQLnMb03kc1ibjwHTcE8DkuuIWOUM8r2S2WLHfS9wbOMEs0Sr3/0X9jhEOV3yTK0wOMjUQdp5WF9hoLNqIaIfORA4zeOegFcv4BM007QZ1mmK89N63954c4JqfII5MXrgA+nd/uVx8/ySY4f891PlspSy7yr4A/uyfrD3zzzCjD8OHJEuICUGEu5QBuJQtQ+l+/LFGJieyIR6ED+DZkT23fT8aI/unkntg+u5dP8WV9rEl4dOtuVTZR12ub33RsBm/219PO5NC4kfamFT7w5f8+ibGiqgi7p0alZD453sWM2ErkFB5Ze4OS24RghTQ3NsEeA3Vse6V2sipfdEhH1+PrnE7lM6AkRsutBH60EQ+EUdii8+GmpCLFmg8eJYxH0ugDz8oNcgMqsrwhWEYMx0iUtuCWdJfclK5Sh9RXWIic6rSY2kwFzFWOxvltONdz7Pd7wh5vNx93sO1dj/SISsqk0+yYDvIf++/N9PxLZYeghS1+Yyxh+8ePG1Xw6XBnV2T3uqMGfVHJQzzTPZYGZy0H412A2RtvBLV7K8LHNB8XA7Pz4l1Dgk2dCBFlNgWdkx1wxnpaJ7oKgqdqDx6tBCEwmN35mH8lP3GoQWbibNToFLhX94GR0F+u3mpPs8yZejLckfbttrpka4a20xejYHkDvH2tsTrTPixhO4lYokvVf3C6njbq13nC3VKMa3eDdLZr3JK5VGkNkvvjdzBokrLXsoATQDfYNNvbEuKsdkrn8Z67/fyRqy++5TLhd0R0k7Kcs9UCgVCeSJ7HANBgAbe+iXZ/iJPFsXjzR0BkJOlh044qC9FLtmdJmGBoFgM4vvdQkw7fCSZzYwGCV6JjfsSuMvYjsOEzZ6D8aiHs8dNoY4UCt3f0lBrnFyu2fKAeVsN0j089D84xseWvrehusj9D6EpbEXOGHBY4fkaEtyp3GwEByV09AsKjTwwqwCPE2X37t6aTIq8xIaWG47Uq2K7ClIQISfMiQGx8GzAVINzcZTWkGYEfMlVPqUNBXlxCWnZlOMl7JwVZ25woJc+AKXQnbVhY8+HopdZKSP2DUDctVwlYLbSIDpQU6RZ2VeTO0vigkJWcHJt7eaD++3CHi3tyDfdMGBMxSa6nlNe/obJbT/wl/HzASsHVVxRVRNhh5NwkKJ1EefnhA2en6UPZJn1i6SYEOz647j8P/VhJbk+Z8ftQi0/1hGXnhXxZc8yfnVCysawL3l/Hm7sycFGalT19cxBvWHhuIDW+rjjyzxWWPn6P2HCvCDTyZuAUyTMaAYDOE6WbKqSpEPSg8ppgj2OtLM0N/hF4VtfMB6bCbU6R6o+2IVVItRaR6drZ8PbSiQmby1bvgpRdbiv9r8aWu3D2n41nKpj+5gaFklnAN+NOIgFIRDWTrjLEWK1+Ys9GDs9kKdPXEUJ3ME0+CAsU9YjYTizE95cNVQDncPsJh/xhg2mSLcyM1fDBlQhp8kmF0qiudVFAg8HHFEFOEDuC4i+AHTBG1297LEXZQmX2Gur2kjy7m1Rmgp0gKszs8//lTiB/A8SvtFFdphiOWEaTRHndhbGtu6GqtoTMp7vUN6KrdiUDVrNm19PGSiTu+s/E1ankxpU54zclwx5Fuo2OLxH9JDRz3qcUEWz8hdR+Rz5WHz4y+whS6FmHBLScUgFum0KJ4lpDBfsx/DGfoTqMQQDM/53OCu9fQHqUUi7zw6nTGWBJv4lR6/pVSHltS5vNEH82m78y1A/Zdj3W2HG1Afm3Jwuy3b8wXxMWTRhh6YEZSH0njXmjIjK34UOr72Zj7BEdBpqeV0IU2fIR7TOVYR0KeIGivUQFB9d/KYBcnInzp0ZYg6UwR+lY6Ji8Fxs0eE7yVWChtebpEv3CiTKn6PyN86kxI1lNYP0F97fTu7B9g4JmDDrKXJXdAGMEFa9IeTxznSiQO2KzYmppAGlS65hVMZnqb7Q8Lrio+sGmjgEiczvg5+uWtuC8jupxZ9CVwr7USm4tLx7DoKbt+563JNkqOcb/rzeKAniqjvfFY9doB4IGqI1Id3m8ycqbTXdHXDp1DazxqFMp4LLZys23JcrEng8nDn4Hhg0TShRTzN36OIhPdXchBeQD7asHBkNySehuXdT9WWjS3XozpSGnbm70FHpReynV3y2m+E8Tx3CgP3dELP1IpIjgpU/N4J65ZozKxoJBeM0hRMy5nchuzbLw6BzEtHUlaEOPY+rNweINc+V5g6+LFTErZWXXn3rR35pr7ar1cYL86xp+IDzuWEKyA9y57tghSU44LBewpclIE2nROU6l3OznIJKKC4/K9lVEvam8qsO6jjpUkFa/qjThzyHxDR7Wz9a7y/MHLMf33EMx4n/b/Ny42v0GRFUvG0rIUQLZ+nWwMh+zqBmoIBA3tSRJEMR7mJ/k/oLOo20CvJwRaMzghAjk7nyGlZOC5Hy8Dy04jWcA/C73bFQBVwR1lPgvYYXMpd/SWA/YxJzgjgcWT2nqZJg8r1x+TMB2JjuobvImVjUCzaMmGMyRzMKRvq856XzydEtAexZzOYse6r4weVfHuG87dvqidoF3QFt/P6KpaxQ/EkZnA8FDwjg8YrMy4axlN3TTUI5JIrDqij6R4cpIGzPSik/G9QvtUleC6l0r8xexhTO4QKCX1NMnnQVuOlOlf8nK2aDXTci8c56Q+NgNJO2cYK52WU1xW401iNmXOZX9ZNPtkrUIwXXyOD/Ms8TF4Oy5IyHI1e3jZ430RQWHcZAkroRzAGngeVtTfdhaiPyyvatNGrnGcvt51ctSqLxtzR1P3YSM5hXDeZCwvLfsbYBz0ooOx2EexVkn9P2YOXMQV5ie82mqEPjj39++EYhfnulH8yOQ9+WT8HzvhPG66Lk2znAZkPAKWG7i49kIyIAO0D0vCR0BQvegXwWw7gDt2yKnb9YMd8/HU78rKPkX37U7jWmL/kP1NoYx761GO1iSBYgN6P2yWOh8rIZQyYCgyuHZG7X1mpVuNEVfJLvhyOcO3/nmOP2ckKR7AbobA4FGdmcFkXHJOLFRTvagIESdYdtybJxPqdbshpqCicF2Fz/6jWTNP/G/pXoc4iNkzIOb82iznM/ZrF30Rhzed6qiYZozeIsiRS9/VxYF7iobX9L/N5Xa7p3YeWfTN9djUcGFCLNpqFGoctc8txuM+mGRDedy57kDiQRb29huICuPuhzhexiqq5vfZoslNRJzRF+ZrHSA1NACYLiWj9oYlDJmdyOU36mq9cUchDjWcgstnO32RwdYK8FCncqvARnpLinqIGGyKOxrFXM4VjkdK0O9Y3E3drRvjuoQjFjtXmEmQVu21t/BGdBaSDhaisYxswug3Ncg/O4d5guw19FPvKPkoHQyJlLtBJQhMblWcCVAvJjywo7Nxdv3CFRT52qJ8yOjJgppMatIwbgd3igAuBcZWrfRDUqyG9rRTaiAi89Z700bfRLT2pydmU+Rukr3qpyfmGqufRPtWGzTIv3qHn/j0lgQgUTX7K1TpL/bdCf5kQY6Eu3a28uUS8XRMyD8NmILwCqn5dJXjT7lIaftjTnR2FsKYJdln1lDL8KkZZNAJPArrw9tL1qoABK/dYw2O/GTe70Sc7lZ4noZvd2mfRhQMBwUiuF/HffDSFIxtHOe9/IIhhCfQL/sdF8bHphdf+mjhVdJkFOezSDlt40GHwevXkEQv3FK6txMgsDLV17/lPh9yccrLA9FTnDYnjEFwZJnc7JrVhekdU6CqLUyXBbiCh7gomJZn1hcTdFmy3J+JNmqyDWxcwyMadvVUaCcW7BJU4VtGDsWi0QDb/dWc5hNqFe93de6jhFwAHFz1ewtIkDcZNcjEEu2xBpvnys2lOPuRp22xqKaFOEcfCkIurw62X+/jiYJ6B/e3YjNVq2KMatU2VE42bI9dnWTFS/oDL6pkZqSN/NcH5esVMkMjzsE98FbPk5HH3R5eZbdh2xzxo4NF9qQMy5bsfj3F912kBVD2FwR3S9RPpixM8VHygJEYTuWoY7IhMkAD1vanMH0HGqhtkI/IneDQdXEzh7UE3pG71/Vo9pf1e4cgva/HdnevwnkN9iZJHwcaHCwsIm7eUTZC5gXR76fky762SWJrn/JsOmduLE2rSXQE7mnFEHzAg9LsPqYXlqGBQDMSyph1s6lhcbJtdKbucsHskif/4L4edaUkA+apoIjR0kEUuGJOOe+QDyIy2f71quIIKy9By4GktHIagcMn6GHX574/g2fqxMw76O7q88ZfDZbSN8/B60UjRb3cCari2fl+0/mFltVuB8hvWDcBoCsl55TgjfXgnG830h45pY2sVwcVPbfGq/8Y4ZPdHsUyHFqi1N5o7qNdcl5eUl5wXPFsqiYlfZbAAnL0FIUx4+E+uDBIofyGtdgWymk24r9zb7BnwUY3GuuA3jcrcNFPQtvSSmts75x9yDZecOFrTiWaHQrDw2fnQ8SrgLpD8+lnBEKmX82+osYAtaeXzJMrnwjd1LqUiNL+icPXKpjyWHN7zPYvW1YtobdR9REaEKckDTY9qurGiVE3b5CRQ9OrDrPLf/rlfCAgXpH1MxdSxmCsikJtv2jhSCrnTJbIKrt+GGDnYBqqthQf7X/trzamBBcDAJ5IAfwrlasoSPyKx1v6aO8uA0X/bitknXRPUTNP4xtQ34jFUJ7BDS4/i5hDLfygz9eAURyfqTMDKZSZr32g1PBfHWuv4sx1tGKuNqRpqMStNFbfSFZ2v/Y5HyVmyNTBlCh9STNGbDohxw/YF/6Hn5IGR4aPFIBoIHuuJI+WNVAhT75dWi6ojQYv7cGRB/ssPxkfjJ8JrTD3OGD08+ctXCU6524NFPVNUG7BWKUUnlOsP4mLawpIrfnF1ai3yp6Sybin4ynMF4jxd0+n+wUHCaA0p+rJhdjv0yfRCV5ry3fqBg2rkD2wGyh4Ge911UxarLTKnD5EagUYGNZ1X61vJAzBU0JbbxdEwtg1lr4qM+4L7KiWm76FzoEclrk7ZnTVEujJLXQDTLqnUIKNDm4Y4LOC9IEYXBBJ2q60ewCIdtDUHxJMAj053Ldrcf0DugFyk1C9ysTD9+JF1aLGmrU2hydlEzW/AT5nHs2zUU43+OcvVUtA2e0wACR0RpuL6z68xSCEWfjYJdF+YIw8R4Z+KkWat+HXj8yynza0wmJBIccFqhI9/uWzOzX+OQczMk8WU+UyutdE8/ISXYC1x2ZYcE0OXGy7eKMl5CYWFV5puI0P6uELgG6mbyzF2u8P6LCuEnsPCjKaHBtJQrnr1zYEQkZig8Ot6aiO75dea+tqCOj/VhVosW1V5irTznvrpZUEwD8/dXEd9m9xOursgOQuNKgHsaCi7uwlSXoPbJ97nyZBQPQuc8AZhFhjuv/CURh5XXe7IMyn+wgxxIOgUiQzeYJ+xISX0fbd+e3KJ659BC8V+gdy11b1F8RzfE38g2T/Sn7gRmu1oUAzAfO228aWEwIT3OrB7KMHjJP6xhs6AslTq6NUwc11mAE3DOFbgnry1xmg94qpDWnNnjQab7nECFiRnRpykpbAgLzC20dsHrtt2/Hgb0qOqRVT/y1sSzI5WchaXGGD1o5MKI6aEFdcG4/tq9AW0Y29ffb0WlylviPY30063R1x8bLF6A/Ac9Ve0mXTmIWAs4ZRNd1mRlBCQe9MgncTI8Ly7uvx3BJeVZlyzFHpXQw4PX2SzzJkX7lSJ2s9osvTDAF+jxFgEPRD0wmYWSZVlwl7MM5X2xkLF0X/xelH5y+3VrWKs7H23XsGBjwjd7PABO5CZTa7MuzVKOZA+wz+fZOzi0y0KufK5cr8vVcbKBULtZwn3kvDUFM8GycQp8/R7nAyYQ+S1BUyr5lIKC5ySCuMr/fay+VKm9OdG3Sz/jrmIf8f765YZTEhrd/G5Q3JnmSXV0t0RgLmb5iF1aw/Kvqb784i77+P15jn/2pj98wW4C3C3jLh3ZF6Pph9k8YAkSfBxOjPAL+lklvNH0DTuxPH3vc5VxTSvcLU8IAYNqDyRZE29np8kT2pm+yg5/aeo2t0g/eDBz2mJtRW8a7P4U2Pb5+eIDeeCuhh0MM1J0r6yXiGW+usUcKpEqWPiWhG1Wv5YIPeD4heoCj4W9OPb0IOupZJt0yzPrS2U/xtBzk/nmrT9iMila2yVWFro6+/4ZVjQFEnxjRwDZNcupJ9B6I87FPmq/v8MqqbNvH7SHyNPLQk6ijLuqvURqx9GcwD8yKYftXIXqN8jjM5H+ymsx1FzdUPpVdVx6NJSbLPoZcd3pfDelpsxqPtrrH6CCtI1td1aaqdoomCq9l1TSFHexIID+56oiyC+5tuR4DGPacydaUn5pWeM2KZcXhnkigQdnJbo8ayi4rw7cSOwQ5NAvOR4pH8Wdm9C5u8R8i1Dcg9ih1pvLhq70ooPqj2RycOlYBUdPNWyHmr62GkEqxeIvWS0MUCc2yp+TSq37Py2O+7vAbSHVJ964Hls0ZgpjKYVz3VIzqICOmwraPSetm5u5HG4Hudg0NWO93j7Sff8I9oU9Gh7FASYmNCCrN09oNy3YaQ4TFSA3JEeW5/XDvA3i54HdmtI4GxXA21QQsgWwPKvp3GP+EDO0f1yASUtix+TYGjLOD+Jj8kuVD1+qRaNqvSON/8qSRZZd4e4eiDwd4RcAIgrmqjqcnjJl6ikR2IuxhBHb36enbIoBB3ismPFf10tyPScZKd+xkqmT3IFR3Q8Nleft1LSS+61pTPVKN2MgkCe1cHvLjlGDgORUVsv/Dw0FYZ0QFN9CTs9HFn9bZ8YkBIPqf6DdkGTVvgZzelLQQpeETq7NhF2tlzn1tDmTLPDOBk/7k3xJSUARXBfmyxTk4P7HxT/dcy1Y2lT0mcwp2w2PHz7NoKuRWIYRKHqwXLtmNNxvx9b25CGqUQ9Ae+wQJx6xoQYFgIjfGkpHBsrEmYu/Xvekq0J62ojhIqp3waG7Q3Hq2+sX5eZ0goc8/6gZcbvjVgDXqzvE+THIAM4HiEHoXEtENl82/Bk/f2Dssnfmcv+mdZ4iXcUoqNk7ogp7EclJvFyEcqtG5Dsc5tcg8dAwdp2IT64LQw8zsSDf8aYnKGMl6AikFup9c4W8G4kJ+4UjU3fOpBuc4JlOc90VHTRJVhsHjFCg1XOSNNs+DWzdKfQtV/AelyK9kGXx93WodW2BHb2jX8rvOO7yQDVvcq11Z1PXkkLw4RiIB4WHZzGxRTBWxOE6SqYOYW14wcp5jjfXz32Cg1Fk1V6DVbsjAYAtHmlUtswMG2dco8x3/2ILGth9zrYI4I5YRtG/qIhG7n2LaKe1avbybdaY/23sHpTqTLYnH8LtS/91jlBgP4Z4u1LUd+VRnNh/3v1RuEJvr/SeySDm5I35f76qw64Svb/u6Zv9SMUz3IWvlu0H0P8SL3XC5KGq26OlFBdzuG1XUvdS/8G9d7rWp05f7ho7icYGpx9iVZIIWm9ryBr0oPUfJ46fK6JBGzPiYR6FJa9UQ3Rrv1Hjw9pHqAeyYaiv02dBHI3zwBszfWiM0RzD4i5fN05HkHHTY8hRQEfF80jQISLmzgMKA/EgQY4kmRZffUWUlsP0ZXD15ZNEQjtABkxBV5VzZpPYu7kK4D7meAXyhsRfJL+OAJ6aaDQiWHlYdyzuUePd7bIpL8wfXhwFfgXilSMCPC7bnHAfOVMEWAhivTbN32UFidkZ+2gwQduLGx0YcRO5JNXxE518POe4n4L5EtuwVnOE/LQpsA0Zw42ivNSjAt3/D3QIMRXc4UK7TDdseg8oX7igGcLkW12JMDudYTWCt4UDUtUJWqO2YnTxmZNpO96wzIIYx9cvDrjKkqU9TMb24fOeHWsk/kYamQZwPIRRtdFczgjdhUj3U/r2QAjUlB1DinTKTjqncgerYQbA6qTMNgD/vTe9plCSorm4l7Tj5s2/PqTXbks/0rlH38KweeMaadnlIxdOz6w4JYAbNmEU6vleYgzGFOI3wUJA9ebWQAYksNh3l4V2LY13qItW1jAQGK4yoRZOV/tus/bSWntJ+8+GNq+lxcYLpJlwsr60g8+3eqOiUU5K+jcgS/FZtcOA/Sx9mO+cMQjJqaBrf5HTNeewk5q7yJLU3fH5s/bnaCtBaeZoRld8QTaEw6FBYllelH0jfXPObwXmVIq2+d8f6gViZ2JIVctHOSanwjxo9a5OPCoqrP40T9gERIBqziTO4hZuBJI4UZ4uxn7Q6OGZTm5NmUqmc3SlJWW3NVZXa7rDl/x++lQ+oXvCxLN6Uj3hvmUxlBxG/6NMFChiSihS4jkAZ13BIxkCwmcgTKQNYu7qt7JeZPXUtjsYpkSAC9EWzlVI/kNSkNJVmLrBwjs6Rscrjt4MNUlUoWGpRj0RKzDdQRMjrKWQ6D7CNKjZVMEv2JbnySsMWYP72q0nSY0YUNKU/q2FS3vCYN00wKsnSx7y74fXnCoZe6vzLyyG2/QRjAv/1u1n/ppHU/AIhJz4/ZvRBtR03yHH4digOMcr25O32L5mGaAyfb6VDdr8eSRw2hJLMqIPG8l+d/Si8J9eHDGAXftfbWoWaJxJ9wDEDQgeAlRvewK5yzi/1lXu4tX/02oMd8CTgFjnoJnB0Yq87HE6NLLWk/cjAs33PQPMudfJ7A2sfcgeVVK/4dHcd3hGP2ROUdiF5LfaBqDxDbJj1ledaowW7jCJRyd1R5xBMPHKEmdM9he60nb5p+dstNqOgCM6UXsxZM6yYw97ZJrSLJRwpdmOKoJ3IrksmVzKTxutdUackfRdcxFvl8U1E5KSSGpszHGFv504ZwN6yNA9IfkF3N3zrtET/Gc1ahdsBW1rGzwaE5pPzXUrXu6w4nvnCADScYDMGieujkpAmkTVhYZP4nGgLJrZ8e8iN30455LpgTYVLLffIgJHqhfyooYa1oC6fz3aB7/tgkcH7E3iyR0OenKw8yvHWugAP2evwbb9bpoa3N1XvPopcUyzhdghUvZRLtEdTpnPL6zYChl61oHDSVqJOpre6gcCdKMqEBcE/qI9N0nrYc91+kL2Y0VVfvZc/lqNOsh41SgPsJ8L0pfYG6nunShs/1b8OBnYdVwX6yE/4W+QGIuT+2mTuqnHGpsvV7V+uw9SDbSV/3W1BpVpjk3NzVSa5ggwi+Bl2ApVrvLl0iGM1ECVjkIX/RjDkFKQwOdzoFK3OUACLMTfzGBwNQB2ozoBTD+dK/SEGg1a3d1zgl/BdK50iFrBKvbKd9hmeVkbcKGPOfyG0hP9t9KhB8904w7kFLtqT3XiVwJwz10le6dTcnlxX6My+gkl1Vv/3olKORfxBb0oB5C0gOCvPohPMEvMSNknvTNKBRdmKCpgQ8oM0iOkFeOxKtfdlZVXPJW5JU5D4UaDmPSMzmqYFhZ/SJFrg5GpIbNRHFI/YozaT54qzFSzwD/A6+2bIPY2v3HAasz/RUqJ4CWouhFtqY9RBMkosQX7NFVG+rYzqdWhxh6/g1PgN2njn0UbmHDmBQiqidakQT/fKKIkuYhqw9f48lrKfbqqixmBOu5uFK8ZJE8tqGqmIsc4UI22h1p+pAisP0Bep5dka9HE9bX0X7A1WVer5dBb+mFz1bLXH1rR+2W7t0/r0KeBQ1XRZEO/c7jvsRP63NJBbRg8IKQI5AxdUVlslnMbzCfIGaooWRaNyh16Wsc4QRPWzXfiKbl4Y+lkWLsz1TbI+flLy6lB3iFZA4NMTTzkhY1DamInbcqz08OkyEeLASwc3UyafTudnBGmnBXutqylDYAapDO0myioDkjjHYeQ5sJx37HTfy2RT7ddwSer8vmMzk59s2o4Rtb68bAz93iuzh66SBF/9kCs1rjP9gxHxGW5cfXSaZCTMCERdHtiOTKb2EC0fPROEMjp0qvU0yPb2MOSzGW6FF4wAvZk74vrvukBb+v+Megidk0LSIndzocANPf+ADrHPB0Z4K6/9rCMc3tyNBEKuHI7Y5CYbH91WOY8/tJWTYdm4p3I8ISliDKOSvUN6YmPR0DEegwcggD1OCMSYdMwHzeY0cjglwYKyZdBlb7XDILilyB1HyDQ5JiZRs2Dh5FKBMzRQlQVBM/MFwgkbaS/82hgEfJyhEGFRmGgY0+9FdJYlC4ITM7GUAdkYxRR62Sw1aGYUDDWPvTFgUERbMygDUXcG3hAH+ro+hwJKE0Mre7cZ0FWqEztWjrtQFdioiLxdQDdBSzf+6utsFCsbxLKll9LR60z7cxrkA2CRPVzzmLZU3APCLO0mmucUXd1mwc2EDE1JvAHs6n7x5HTK0gbKyJs4jCRBj9wP7weeiii2G3zfR1XWX/RF2juekt3In6SOkUoX3sZCnMQyjLteUfqsHSq0mT1HSA35EmGQYv7Dxu7BhgSFN6JGAtncjd1c63zmfps9ZCEeSUWVLrSSzlyjrSPtop5B0ZnB1tXJr+lJNbE8tWM6pvnjwkO06710goU2T2a9JDOxdVd/mmEiRf1S6tF9bRPwS5kKBCwP9M6jfy3xUpNmzIE6bFZ4qB6a1ZADYw5YNz9s9r0OndA+f5BZUlMmWjNqqmduJO7zgZ+Ssd3YmoIdnoMHlClqA4827xPlCWMBNGV0t/V/7dBvQX4u1Gnva0G8AzWRWkHv1ZtNTT23kd5+38F6ejfYkZLYK+ZDVKs1bgI+saYxD+FmkEyecwrho7cdzwUfv/K9K9H+KrQQ8251bs2fBj70HbNXMRzpfAgCVGTEMYgaz4vMFCFtrXuf/wW1KneIIi2X5/4Xwkm0L3q5bRrjbsmPcCjLvvaWEo9jBu9X56CFNM8INcWcFMs3zqr02cnqRdWivibb8eSzOzxNlJbUy6XrAG2TC92/n1Po7lYaAEKNovoym/hPtoXUUsFPJ9SsQs8L7T2dxIAt3cUnIp3UH5K6yJrpie9CtNNPWaG0j5A8ZUmJdLJll0wzWl/qcIl2dGeeY4ymLHt47QNwWRX1V6i2T9NERPerHX98NqC2EFhbuTAbOZXMlVd2IoYvtSDQivL+9e60TFqhyVDAFIC2d2rjrVY5dAQPpdBOfgejtu7AhgS9iqKvXZpgzKdrSgEaxI96msCIS1u1u2mgF3zuT9toAAh173x5sL3isJ1N/tTfg26yoDB7jx8FGLcVa2VTn6qAElbydgfAQtQtV+hZJOPgWBSAXUWRiUzHM4qGNXg6DmbTsKXdBbXOdo/6NCV0K2LXxarCCMH0WuzpIRElB0ctcU1mGGoF3N0Ef90kTqySc756iw1gcUk8W3nljADS6GrDRGzmtb1qAR5oJlxv3anJZ2pqfARIng7+UuxXKIqtPBfA/vUnAwUwZLoO3cLOSK2wrj+Zp0wAYnqwb2qAX3lyKKXsAZNheoF371IyDHbHz5VL8yPMvBwhODW5gUw0u/7VEPBu6QJlUSKsfXXuvO6IIcZ/xNXhtWRA1CM2RZE2edOXjOnp7ivDcBzRRh/QI0nknVlfBsLZEROH7IUkupYlp33t1EITNQT2Zpz36BQu1/CiCVC1olxi5ts3vliWYN+7OZFR/+xqCZ4xe10woZpBtHa6B9IXhjHT9pEWU84RYXfgSa6iHu6P16z8mwo7Ont2YxJ1CGmzgnu05lGHV+NMR9YU0uTEO8EUZu0B3GbOBVQay8hj+o2RTEOvhxEE9zqKYFrTLZIqdWwne3nEXYYuR4FGBWdDG04CgZ5vPzPhMXFQmOPDohkF1ny9KXBD61VpISWniI5ooO6MsRGBpJUmQbP9fl2pI5V0Mr1oEP6YavMdOLTXtWtj+EJOUkYrijTtW+Dgx+QPKp5Y7Z7lAbIrHe/SZyfe+VwN7X1VnvKCbf+mYskH9WtikPACr3W3kAMVZbEhxQYbCIcFi1G3ryQBdFbFZTD0PLqSosqR2zz9J+LvKJWnGq6nSPZ+yk795eJAz3WD2hdELDplgd3p9qcQAQ3oXWZR+GyS9FP3zpM/l83kcEDvXK3E0NgcfD5dbViTsFQxToUhr1psHQjGdFd2CeUqWFubwRzpOS3jg8MKnu1l2INGIzSvv4x9dodjQR1v9SikTiutBMm10a82eeDBU8ML0xwPxgePCwuYGpkGiHv92wcer2ER2l82EAdVhRLftalMXRpvoHD8wPYcwt3Swk8U+CgGAPJISodqcQUlvCMMeE5GVSFGs21rh7Q/k8AeqQuKGvDdOixfT9CmhWh/gv4f9dsT3jnX0DjKeRL6Mo10ndmzjgjA85kl5zY2Al8mn8TC2vQsJFlIPpMiooaDT3MLLCXMMKZIBkn8qX0mZw2MgEj0noM45c/4w4pkwpfzyLc2Iu8T2CsHD9nSK0+3Pk6ei4BoJEbh2c7TdjQqcCF3TOiE5sUDeEOmOK4ArQx2QOQ1mhVP+XXHQ4/4DgHz8QFMK0kpsba2o5Zob9AobYJhnwjGRXM+vpR0+aA1vvzzqUvdVoM+uiR5H8/nTV7Kh1voPmsRlT8SolOIQZ/p3zLO8PXnrmhwMHJP4TM5Ad2S8BlYq50/4fdijRBRby11w3k8r7LbM8TguSMYpuTsqs8448YVT5uFZ0EyFdBO/NX5R1FzEBS13uuyaKqkMHbuyu+umE5Yo5OWIemUQ7MjI+oZBEai1HGNHorAobDgJyDK0ByCR7M0DHaDKdFGYT3qf0P+6rtP0lv2NEefX9S63dOTNDvBJXzR9hvUGs0GpohCzec+W+YQ85gYs+VMf7sYcssyO1ss8WO27jFTK+etJmGa6UmGxTpjpka7YWTo0CvF3JkKqLutjchxMD+XuShjq9IsHAgaguyBRnVUQ1D2fMDJrP5kQKY5Jm/opFS0E1KbAaLv0dm0GwsxVrLblZM/YL8W/I/AIrIdJ/yEwr1rfO1WZNwaNG9X9/40bOfJG/euUA8jY9kuODxsvq3TgLoGyimRaqMpzbYoP/wCjU1FJ4wkJoGgLfhloYXJ1mtnclMrP1kxxMAVtj5NT190HLlt7IHWlQ1jiDr2Poj7XuzVgzt9BKs665MewJAmEpJbKsJcUPZ/XOPGCNc8LiaUnvZg9yAf9jLRUXZ7nmQjb+sSYY4RiygCKL2mMY7mBnJzlQG+PcW21GmkUrW4RQpm1c7I45cot/vXwnsHKL0isPfKoDb1dOEMi6XfU6GkQHi/Qsyz2179f22rH0gAS3t8ePJIEXkxXzCmJotdlpbZ9j5WnxkUXTq78cKL/JBsQkQ77CAbe+X9RiMIb9a2ieleA00Hgkj0Ep6NCiSO54hPafQeZMZIOhqgjhFKGGfsWURREudzXQJ2gx0H7o7tmPSaonXogTnu2sUTrH9/W4xIMvmP3o9BOoKGDJWFjXO5u6gyWmqqNf+SoEFGr9f1G7qYP/X0aSBL6PqoS3UirOeWvOxL+3qMAvAPmG9hUJ95KLNo9PQs4R9usOSSPyYGJ1+fS3WuBXXy7oD9wlf80GYXL/wiVuU+6VizxMvIiqRLC/K+GW+Tm5++KjhzhIs87EjsUUCYHvoBwmR/Ss0fVWq2fD4xu0mgWqqJHofiY4LiJcXWwK13yVgaXetCA+pUZ4JMP3HolrHSuaL4CtjoJoSaYeM8iAp3+CzXjBtR/V1GuJYHgLONEehXAo0L0Z7ipFv0cQbGB5ss8HbLlaampla0EOd0PVirgF5KFO19SOb3LQPj0LVJPVspkWGYvy0V3Hn6Uo1EF8qdH6yktcNhXLF7ehv0Td+Pjy6uv9A79XK/OoEI5lvDXWX3JWgfEBeXufeZc30Lh+Mz794HJD5Z8nlaMRWPSofNMzOTN1q7pBk/+0iHyi5dwO/t1dtVRlW/FpAkfOIiRmVRefrqraRaD8bqBeFNRmA3G9WwLwJir5pkIrcSnQcKbqqnx3gC+Z3rjJCW4brbBeyiqREhohWznSSNWWAcTH8nQEJB/6+XU0f9mHG/Wir4tDXDMOSf2hcWBzz8F286Jy+VFuaqSQ8jpGk61Tv3YpwjyhLIv07YIwol+LYnIt0cQHH0ySeU/ymi3tZGI3394S2+LioZtoAqf+ZvOfFogrfUA25e+Dmxz6PPEtIvpNpmP5Rbh6KGooommdujSBeve6Z+Bk+0l3t9TxXP0ZhcsE7ov7XzktJ1VZnc+HGQeDLp/XtuOV6VFL/0ZVmz+bnOm3MzcEXRtVy3ywg+TOsOdzJNwXIP34t2Rz8mwNQgvfH8L6l577TEbiaqYVMcr5jCvkgDiF/JUxXqFRFyfWlxVOKd8AR1PzZXLh9GSYbv85uxAjSSIuBp5H91dbBdE8c+BHchfZhJDSIxBX/Izx9P5vmxABTH85F6Trx9MlKUlT9JYczm0kZGY7l3eI99njj4ePOnkfuKumHU64rSe38nQ6awbKOUPIRyBo9H7eOgZ7h3lqbHEdMaO0OmDovXgw+4y00/NyNAM8j4nScWztlGP4XySzuV7d8BmiUg8eC5syRR+xA9O3hILausetAebfAgg8b1Z5r4Rl4Rmqv/BEje486MkXFoCMzj3Atkl4Bnvnl+fE0Y3ggk2HTd3a/J4VC9LWH1Kyi5NcH4suJeojPa5sCvmZNjNeWudaUxG+Lnnc2N45msq4lVfkk2lWpurctyNLyaCLUrYiG80HmxBZaU1Ukq68ideUgdzqmQsR9uRfIWPSr2+zCQIgZppr8l5u0QyCLte0CwyxKvfAt8jLBmeEr7brNE8YDOTX0hCaGeyogOWEIbstMTcAIsPEbmTGCOT/GOphHTIy6ZMn5gESI8L8Zhz3eh5GNaQlNoMv10QLVK1zMmE9ujYoriuNA8YvVsKDynHxvZqQ+lJPAJsIVhTTrpCqKURzjlAVRYhNlrkeWo5WOX6ER12yXq5EslGSRHWIeOGfb2ZC9seY3r7RqENN3dECpDAMHF5qrSGzRpr6S8fjei2gRkp00sDbt+euGC6Lrh17LHd/O2owW/ffL03Lgo2Ys67iQOUHKh4eM6qp/A602COeFGluqN6u178MR0pS62bwtMfXSyAuD/LDx8kfcDqnUwGWrYidTH87DxnwNCgtVmvNpGNmw8wgLFEZP69iXa6wyAnuixC82VM3olJVPHyZrLzZSP3yuDHOcCk2z1t+0AvOzROA7uSf1Xfug8KTxEB04yMu6qJIC4cirPZ5nDjU0W/s3FPaA7Wp/EvCEV5k8Vs/s5GV3namw42LkpCfEU28osCuonZKGspdvOoKzQpFuIRN4NcWIiLvemOTyHfIFabTSJga/G/JTMaeRP6z/SOR8J0MpCM64NAVm+K2bTk65fJLh5aaX4dguKe1NKf3Ih3CHdzSMUJRq85rbC6tUYPfDM1PB+dz6vzdLIk8cfhuiPg2xFlRtAkeHZVl0vXdgrv40knPioQXz7iS6lDgii3X4Zm2sPNe+Y2825UGyK4BKKHm3YmuCEEoM0qcf2cJGkLcAoQD/uTqb9nbJNvLvg79ITMvH/GJBj6hxblA80u/c3Nk77rRsMpsZRaJ2LCmVcnMaLd/L9IgHuMDYjqNDzyxsHtvykilY7cwFz072U8PEWenM9+Zd5UPAjamZ3L1cmO6TzifG27/eGNhRaut+P5o+K6mrSmpxp6L6KlgU25/XQ5GiEqj3dzm2Ew+jIvKolJHSuLxoyTIA5EFbdpRp/PIyaqwyZzcmSzKBrrRUPbXKTS0h4rnKMc+q6AlQ/UL/RScYuyNJ8REkQyoRaJUcgKTF9A5GK/XSGWiF33axg2eFKC67sKKG3plsxX7P3gEAkXch9Gz1C1QiD40E0EhP/FPzeVYKbkFRxryuxccvVqdkH2OCqJBL9GLm0rkoaKOUmV9szZmuEP8zW57+gg0d4CUw8vRzj2a5tNzsembRSPALsKHgk2VxoQe6LOygctY6/10FF7V5Twiabfh9p4wnm5QJv11h3V3VpEPcR/4pNhRD31x3pUgQtKipCKisNUxj0m5MhMUvDyQaqDjpvUapIqhKnCC6uuONSneQa4KfN18cqRJkMT4uhDCK5lkXMpsekGArcEKaPfnT9qGiXKD6bn4M+pAIkXbdcXxFzcpGbBdvpIrY8+HlYBXjA9+3zxzmvgFxylRkFaChK+nLHPNwkRVSmWPNI/G3cXeualq+/EW4vLjiuD+JUycKw9HsNfDMQKP0c+72Ccy+EXdDsJXXwPeBmMWR5uvOcTQ1JVoIGrr7J37eJgaqJeq6htVqJawgQSzKQl0K7KrbfFdvJcPZwctunCcfKxIlKM/CeLf92StPBQ3n+0yi84u6qE7R885Fuh4FwhDoS5Rx2I69SY6cY5HDueEuWRkca1Ag5JQTYoFme2Xex3yr4jWhxLU2FvlxqaVMEiJy5iyPHj5ykrkNW8ueYjg7AWAEXbCjwN+7KDPQBwJLPYrbMBBwE1YTN1TiBlOcfLSbP/8dZtSvdqn0SlhY/bej5OTnEuZHCXdJkoWIbZkH/pkbp1RoCjY+3p37D6hihrRlq/Gsd0WBaL1Ei8jmgbQ4WJH8jCDh36XOUcPkXQdVWWIepRKF1DGy+BbQN36SHQQ+ARevnOvr3TwzoTqr8GUguyXoOxMmnRM4ZlFNAUNVzp9FW0lU51W2IkoGKrA5NaMbsUtL+2PnaJ30AnoWQq6KS4nPT6N73LNqZIYYOY9SG6+ZWdb1ymR+6sdu+Du6bVv8JOJMKZPyamMDYNJZMvZ7+ELxuFBrnWxnyAEJUt4+QknwAxBPIAFXxU/iLh6l+UzAJ1INfVdnw6WXwJAF7gffbCr6pfK7OJVt+jF7CciCRapPuJYMusD8Igx7styFCW/3sBgs/xHNtInv6vjgXwAdfn769/RMJAGGq+uy1HjUOEw4u8ycPuPbzBpmWDSYy++FMsQ62cbBTsaRAPaYpwQbUbWDY2LSJa0gyVz24AvzRWnfvd+fNF53LNemL7SxYz98nUuotD00sQYYZ8GL9f5YXiBg6ciwGaUPTqUg7YiH9HuSuWePigZ13d7GTzInLZImRWJE/JSnMn5VOhgoNURMpNW9FEbFZEqs989lXA3FR2KIB5pCB97b/1mw2r3tMOcSnCr7miFQDN/snUbF1pG50Cl2aLrXQXdcfQKtGh+eBA1AjYv8UlnbKPXCI1B4L9oJIAlhKRXmmxOj0SlKvYZ1QmEV5VTqpeUmfhnxK//AO3GiT5rb7+HaJQ1+dZqS66lSJfHJSnsqq+rWjJoPZwhHIG67Dro2enPlqZkFpX8Hpl/IIOjUQD8t1CeMt5BVMDW/mcFXvollPz60s+WPpfOEu4BVB0Ck+3+rfcizxrbUt3k/aN1eHpjRfN0jrXQqYGPIZfDmfYuGOHL074YJ++Pr7HheAJqzUUZdaxsoHLPNIfrUcKL9mvG8xyM+nvQq/ZOI3DX/WcvVwBUgBAHRFfVyYJh3FnDWdtMvurfiZva8RA1zxFOtvMZiPp5yHmpspSaFiWpLBF3KbgLeoMnGynZUwdMadrgJdHmz2aH1lMgL6sNzPC0xsQnUzjnp0FUcujI/nAi9ZHPdn4pAsacvsC9yinQN8lK9gWEEo94V5fwF/rMMrD3yrtoV6MxDcgrsJa6RSVmKm5nOhlULrnrlyYzS5XERRhOHO0POn7KYtWGLAigs8R5qp6p8/K9SqieK7alBCkSxD0ibF9h1yY6yEj3VHIdfBSBiyPzJm94clRgAN6IdzoeYeqwe/J4kssslBO+wUx1jH7mUjVQl0sMG2WiQOQnIF+pRT2N35hBkMb2Tu/FDG4wFaH/Zas2MXhS2Qinn4WAb1U+t9zUMMN6ma+LrwWO4VCpqowgKdKluVvBSJoTf1/si09oqHxYpixuZcwJ/AEUM+Se5OqymcjzuyhONBa8UOQnwfcaFXiaITdlhBYSQvfZI//zwBfWD+VC7sGlCvfOb7BgfYgjextpthBwVrKxf696SCFAxNu8HwS5tmdG5V08LyUrLO5nBn0VsA5QjVz4MEE=\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" card","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" pattern","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" into","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" shared","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" components","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" canonical","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" tokens","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":";","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"LElKq9fY8CyU9HssDOv33EsHiFJrUknrBQonL6f33JXXDPb1TUnwO+uhz+quQp+kdfc4tm4gQRMxLoG7k8tHFAtj/17/bnj45NO8JAGgtUneVlpSpCQopg4Awtn8FC5hP52gqkFuIHosXKicHP3eyGx8bX7IdRj37bQouPqOtPpORVozRhJD469V4b1/cTFpss5gEMB/zRShsBrcMS/ooPtEkZnk8eMxrmgpQD/SzX2LkULVrMkr9EKvjwLhaxxYHngnkA5d40MPu01dsafCGymOOwTyDuVo40N3rs9LmowVAR08RR+OjnaqKv+yoQtAi6Dj2vyXMDWF0OBXsqDo2MDAsb4OEETRN+IaKFNPoRc55WxwxL1SL0Wucvd2dsSAB70pZmPp7qMy9VP8oLCR1S21RwrJxeHQdgw/7bDfJNijr1NVzkDrBhI1K5RpZl++Z1/fTP3GqH01ESV/XN88rXjmtZ6XJGhteRr/t5B66Qe4BC7oQKl+LvIeUUKFUX0nIJJkMmjuUXl2/uBvz0mdlipbCLxfNmbAkxge3sSsLZ9C8vtPx/lC1GL08TGu/BhEB1ru5VeuuTVlH+8fWfuhukr8iAE6wKlPjuyBMyp7vS6+wvU+Dadz/OBJIraI1G6mNV6ze12SIDgRQu5XAWdhdhLnkDc3lVFkCn09bMGyGwzkSRFpfcCj14iqZ+WW/RgA4FymCxQyVnKL7jl81LvKIVC9ChMBU8pt/jzD5gqe2CsE4wB9+dZUscXg1yAvZo/e27TdNYDSyUwiG589ja8plRtqGxImAIQruQh6pZ558RKSqAbw1KYsJqLiP7kxpu/FlTbqTzSRdpXiPeWSSJGTfrW3HqbDaRE2KY6T4Y43lEKB3tG+cQKgHx7Cl1V1fH7YieRcQoBbGQvuhATha9S0NQnztmcgaCKM88v9Sys7Z1KmteatGeHNZ/qwhMfUjWQPCAQedTskRIwd7UpqcqMq8L1vL2DYquRZ+z5urQiC6YISJtSBVqH7thMlQPnp6rao0Ffw6Si/qFtfZMo7lrDWHtKpUz+M/4HztXUr4hieN1gI7XtsTikDpQQVOYG5Xj+s0BoHElJk9WXZlilBB5JvQpoTjS1zyA2klMtQukSxnCIiLfN5PxkKl2LLYItfFnpfQ21c6x68adaXsxQw3/2UUNLghUEFFtg+LRpdI8nG5JF3d+lhFTpx0VVaqeGUhldGRvkq14KATbpn1opnnJ6a64IYOJP0Or23ao9qDY5jnnXexJUtimOeTWz0xT1y3Q4d0IiV8TLC6lkWnueRgnoO3n45JIQQHQpzn7pe9w9rzoMQvo3rlo8veNsbDTRmFiNm0GMOWwdGlR08qzxYl0EvNfavTGtXxYDqPf4N7GITN2jj+6BCk144gH+z1HlklevNkQqhsguHreqe19x3j7X6KlK4GdP5WQujHTiixnVZtB7mx+b4Hdka0C8fHDl6Py8zzkQTvYvs064yF7H7fCPlXrfuFMwczM8VGVa4ayElG37/tmQwnl0jICE6ZJLXJS1Rkbfg3SkPl1Re3vZiVSRCpjFcETjkBzsQzeUyLSHTHy8YvH4pkxyM84jVFco0heyJpOzlP0gI039n4NZQSWvG4xpsDT+VORtZN4hbZZQ1IrXiaeOKg0quEBw9gcYmywDDhfpnrIr8Iwne+FueTXD6vXCLCQ6FwufmpUa8V4jMARnobO6gC+/ABxGY/TcSVspozjBg/98mcZRsSkfMNIvKdqtIxeo+YnSNbsSHIGKBmngRusPrs6w4aackcY+gtapGI8vW7a6DSeigr3lB9sPkrk6CnonnT672s19T7veHQETWfb5O9Lp9CP2EG7qFjDIrd6N04k2OLNTCD5k4dwNxSG17IjU8FxSKT4Ffc/Q8jn4P+GFkj7+81+dWZuy0Cl/qJz3pliVxOuAxMU/4zmUL7zdaAFGhQnw5hqfKyVq5rjY12bUbf0oN9ZFv712j4KkxgMTKQ7hDw7ell9xCGCuA5KVpcVSfT4Rwzr5q1mgy0Jgxw4E//KMKjn9qqe/5sXbzIQkdbzy0wEXZBqbHKwtjAx+sxzWDtANGZdAu2QmE8BuhDQSBqot/R3QRzQzx5RuixrePld1lWflWgkaBwLvKcmP9UdnbXPckL2a2+/LFecgksCvVooN8zyiH1W5YXiRWVKH9TE5x2cI2X7SeMRshGUzZV9yfLR/RKMLTe329gsrWLsCcGjxzFpywHhlt7FAIWoiiRCtMU4arzj4kJjKz26/KKCGCEx1OgYD4GqY7IDKOriMOZuSw1XfXrhxKtU4ddfqXI+3gMxSS4Y03mc3yvonwJxzd1qXk0LEOwF6gj8xoXkpcyqoxNr8mSQNPhJeWhIFg1mlyyIRUVa05SIFKld1qc0AMk1n8/tCvzHpCbFScXL6o3o5b1EJetyGxdRmppr+AoovtQ03y0G2Z1uUR23AtbxkiZWlCB+g4Vx5eIm8e8uLw2s/AG6Qa8cPSKnhgJ2b9i3UKpVS2mHezLGTKHNsEVGxjKoAdLDrEaj+S6y10J3RdcQ5r70YuMAQKoT/NqYjiE825I2IvS7A7W6BIMFMuf2sPhCpT2RPtirK/ppC+Z8yGrW9cZFfGeHVTIetLNP4sMCc4R0ZBGcyBEv8gXFxRyQ1WTraIWQMJeMDGSfPwy8kwcammlgVyL53QGU8Yx90hXIiaUX+zbAmHjzk6T+yL/PrD3ed61A/eeyOwcWq0oQk30pvtk4VBeNw9vopFDJPJdoQayQREDTc6u4mGbj8luQlNtwtxaFzKTKjwm8ycMUxzGPyj38UMeuZePcJ3EAeMIAULi/3p0LHKnmJQliIeCMYZ1INd06BRUaH/TxlIICQveurvXIehKny9mq4DDMKStEkXVkMkCg25hlPVkEvHUB24FVjp5emIc2XCpJfN0jUzM59ZK0EepptE/CC5efI6I50BMs/ssxB/5mwDprSwVSHnHlcFZNgCp/ecTyouhS3BxwxfKHFNSEBkTayxz2i7U+GLA9jQ4C6a4oqf8Vq2KM7i7ShwtuBSZvgPWVWl6Vu5epxEatszjkdibEt4HtpcGaZuTdCYR6hUAWYn8IA2BqWS7gojfaJRV/jp0NDYXa9XvEvE9zLPHR3HBQLxEckUYyaoJk4uYeaUbs/ieR594MeMI7BJjaOieu3fXZeZozi+3lREEcDMCIvy7mBUaCBHZNcQFuqhRkRSA9X4CTy/QH0E9KfXkrAchjsQHB8X21Bql2GiWDZ+cthlVnRGmLLQG9/WwwYCrE3/q2T1aBnA6a0GUmH5OcDXdM4hI1NwkjW0hbY3NcleOh9Ad3LQ30Jw+pkEo+cPggLLwjKqUUGwpVM9N7OV8UqobRPP4yR2pi+57NKPBV+0iddQfClw1Uv6LubKBX02uRm/bGCtLqbrGTIlHDJ1aVIa9QoAon0fKfhh7nGAXxO6zIevC0c2UhttpiQEXO22i7orrmEaXYXTZSPKA8Z2BFlUlaHb2WDe/ox4IjQpQ07H+kaYT/Y72j0nrIJ1MYoBQfPToHd1hQG6wMCLZb0l+HwH/FxvlAYwpSj3u0V9D5wmLfVyadoLP4j7b+Xgd/VbR9DXrwsUskOgZEWOOs8CdayrparTSkQFVgYDDaMqgQkNqj1e/0kiXp+RrcFsrNz2r+icUeZ4DHbfrvTB8ENeQwjfpY7e8GmglGtllRAuSPxzuEwx6j6+Gn9hFehSfUNmO0ZPU7on1N1SRE0AqUZSjijRkVxXUkRAxmhg7GIjeP7faySGAGzViWCethRbzThYKDx5kXKjtbebq2wMbaAIamY/5XQsJmXndySfoATjUzR+Hz1ehCFvqWLuPQzr0pyjTindDIWDHwEiG5jn0zJ0NLcYZ9Bc2px+K1+VG+8vpFJPQkFnyLHCpMwNr4ZiOorfYQx6gbLy/AOSCcVEQJkWWLIh4TEWeuLDxYSaZydnwEfLjpjXgbrPi96ZlZcbyULIhTAqg6SFiXQr7W63dnJKzxrwA6QoV3TYQtGwRL2/03yk81+I8waZBFXNLjMABDY/KMc0VznMOU2HWIhHUpkUWcmitO+z2tdSp0K5CBtqFUeQzxHUllSuEj3J4ZLRresIbhf4gXB1eyIKPb0TbPbthVUDG3UVQQrChJ/Jz+E9mGRDQLtN3/I5IA7gqsqII1dzer78KAktdAKEt9S800p6puqQazddq0/Q8Y6ycVjJAEYshQunAjM0ev/TG4j4jjrZjjegNKfUvwKcHzYyNSLNSbqcNRzirL8Q1cGDANFsj4wl+lmggCD7p9G++uwtsWZ+iSkjzr8Q++A4zFwNB+WzxLlfSuQ12eXV0tEXTm1MW4rZ3IQO13DxP1s5m/TgjC7g5NlsdUrprBqvxSeK9ThHjRg+TKp2bYAiqaxd31bvbzu/r00QW9U9Tner9nXn7trZDnbfGRWRypXPCGTTH4u39vgO79x7mAglMMRV5txmFcgcgGAtPHF3Fri7C9vZfzNFAUBc8AWrAoVKrXlJ1bSelEu+j91ixD5OqZgP6upPQLTgASGArpU4AK40TUDQnSl4uy4nbTLN8RQKa5RJYvIvGXOiFao/1QYczOZWAS21BiEBcvmqfN7/MsjcUwjwMTgt6XfSGbGGukP+tusVeHuKA2XvbBULQfYZpIiL8fxrOKvYBTfhtRQB8IJdrYHj/+eMn0JbewYMQ+xrk2eOYqxTMtImotpuA8afG7Xu/0XkyCVBU1ZjFkpARSRYVtl1Se8RWMRzb7nyU0nmOV5GS0VdupQ8Qz02RijQWDtp/rAwlHpFBT6PfnnGM14N9oFpTc7wLrLXa4W5qlfpGeuinvmP54D7wqSr9G0ITNqFoilyPnQvDGCv2wnD8JN1koyOAT2O6SFrkdyTrD6NBVG82kOdPCBpDk5IYx0J5Xq0L8dIw596TwK1khVAcdBG4z0bISDg/ClFBhVrwHid3QX4kKJZA13V5B5tishx+6syMnkmowtM6w9jQRSxjaGFJu5fDC2SHxMHzsAI0bArNl01J8c3jZgCSfn/PoZy8fmBtMuYwsvRxRUFNmVKGfGnvcdApqVE9Fo8d9iHK/tM3UOSlmKQpeb5j7Komnx2O/By2RUzKvSCoJ2eKoRIDlcSQPJvRM9I8Hz74st96mmGwmhIqqkrrZ4Qfe+svK5mFcsQhRQg8vX3vNXifRa8Af/uuGksDfv+QtrLjGnZscmMp9H3lJmO++jyUH6Vy256tDjrSUjK2RBatgt1FJaPdYpQL/OfgzpMD4D980WvC4R3HIDpdtd2mRP2ZttmjBhAKwsbnAnX/+Axvg3eVnUdB8SDPmKLPibT2t44SW2WNZkIwhTH6CD0Wj2xzntFsBp9GJW7gAWWAymGClQQsKkbWFzFiTly6h4QW0Zf4se4yQQJymdfyAR0bzgkH9SM2OttGiNveotV+KcvanThl36/sNBm0MFSp0MDQvO/Gc5YMaZwvW6hwe32v8+c0WAMXrj/wnN2g8CrPPJ6P/GxN8rCIIov5TuTMD4yLeUb8t7BhEBPmL0YNWG41VQMGgsJTwzN50QNz4zkavfikQ37ngPt1OUH+TRVdXKrvwMQvD7O8u91uvCnU8YQzKQkQwqi+JFYJEFow5oVZlrPcJ76d16M5StLuKjCkZ8/EvN5+FcKgLuRK1m6klX1qKxLG1EjL3ORFbGzsECoHqwfzVsV+lWwQTVrvWmEZ5/CPiR3wPvPT4iNIiHwAoPCBip+AWBOmBYbyeMiMvC4GcJwgLAcU7cqag/JlnvnO45qXKtgptzYfilGUBLIdLzaYlsmwbXiC/8P7wof5rwwJnFaqnbcP/HpV7Q9sHMR8dGzw8r+n0hzK3v3wCvF5NO9eP5T8sdAz9ABO7TR1wTxBVvMqr6a64ylrs6KMwENKkMLQhpjJsh8F+qqX14ORJdUPE9R6w7NZKpp4VJnY3j+S/aI0wHl7c1dT20mrrKMl/QoqirKSponRv/aBnLPmm5qb8pRdC62wkDLsyz1EqJzML/Dloaqa1balI5Fkr1utkcqIyewK0xB6OSKlNvfq32bp9HjQSn362o0V1GZL8B8RMxy41bDpbNcARWGc6SrX/ODgEuy27wGnmTlLroJvqZaRm91CFvqY6/XcuJ/nCafDvE0dpNEoegEJXwJOj0e6DcqLyiH7nY21qElokBOTaQ9WvAonGKMry6mASll3//HegHO393Kpvhsh2oKXXOQ0omF3lm67pzB/P3qI6xsNQp4a0MBTyfda6yhHjxTzWYawqIJGK4xUCYAIS7sXrDChUmu7DDFWadWg1s9n8Y4fVzwGCpbXSpJ0j+BRrEVg3t+ZF/IZeJK31zBRUIOfaSTV+GjPBQyCRGsppF0KO9u0456X37azvIuZZvhV3l4dTvAFXyzjTkodJ1OyNCbCtqMx/zBDbOXgihXPqwGp8i+NI8vf3w1L6Klkj1Vg2UfwLO3tI2rbfHJLzv0/VZOcmkEsWKwQm262yFcCaLwtNFdZ/HJEiiHO+Sgy0KIh0PY322Ne3T0uuH653Z2RsvQSO9L8c9GMxkqpRc/FDKHoWLGiXCMOfteTuZjdKtB27Hu699ZjRfbBj5J/2VvkncJGSw7Upwa3pMxxN6UtW+LuR/DR/2079faUazpx0JFy0jsoZj54kKPiJHIuqXIK9u0wkqL1JvCaWkbINtZCOtreFxxf5t6yKEn7BftqTNpYXud64lf62b8nOwLjAOknqtgY8HQsmZQa0lZh1FVL0vHJgJxCmB48vbgxXZreEA1hrf2VNPAPTrQ82v+UDXTEKTC6uR4f7mQulhGknKH2r2q/h7jOUQ2QJgnqOfPmu69q9lsMpjcYQPQGVPcQAqThZt6K7tYKucLqQQKBMNwBgOHDDwPqmCKlOodEP0WYHZsjm1bdFEAWaMlRbQnI5iAZynXHmaGWX1I6E+f4hzkSIEQlLELp23qKl7cDA7Z507MSteCir9Q6IH8B/u4zG9MvRtAZS3qfiEhWKpLAl3fllEvsmDfDkHfkiL9fRQCCfMmKEzJCG82kDbr1BJ8BMJgga3yxMGNwz9u2HSyyV3elyUasblSMdQofiGWZJO8tI3RvfYjBMMTvzoiUBR1KgjjYD+arhjIw46NyYg9S6A2X+Bmg51AiHjhZFl21Fcz7LZLMp+1YToZoWT8CcfdmN15uIqg9zU3g2NWrnXViM92smDtsCiNKle8teB8pKXLn92tTkDza6I/+C27/GL9EtWnDykXt8XtHhGQO6y5fVWDo51hXM9UIuS+wx7NS6plD3YRvrRRq73wyBdtvdNWZzHPO9q1yW6HEsc+wBOLldwFJq9r0vOAoctztp8UiXB/cm30VEQk2iIjsngXJLbbDtKLM5dwwa6kWnY2NmdB0pBUlt8MyrkFiSUGnIelRFGxUTJkZ33AD+cmQyBxQxKg28RtFpssucCPe8xwB/iS6TtwFJZPl8gb0zpDJxbqkUETN5HaP0zRgJ9EkR70fhO61WcSKpqInCOFMRvq2g6Pmvimz9DoRvP9cMJT9gnKIAAmdOPiQLnMb03kc1ibjwHTcE8DkuuIWOUM8r2S2WLHfS9wbOMEs0Sr3/0X9jhEOV3yTK0wOMjUQdp5WF9hoLNqIaIfORA4zeOegFcv4BM007QZ1mmK89N63954c4JqfII5MXrgA+nd/uVx8/ySY4f891PlspSy7yr4A/uyfrD3zzzCjD8OHJEuICUGEu5QBuJQtQ+l+/LFGJieyIR6ED+DZkT23fT8aI/unkntg+u5dP8WV9rEl4dOtuVTZR12ub33RsBm/219PO5NC4kfamFT7w5f8+ibGiqgi7p0alZD453sWM2ErkFB5Ze4OS24RghTQ3NsEeA3Vse6V2sipfdEhH1+PrnE7lM6AkRsutBH60EQ+EUdii8+GmpCLFmg8eJYxH0ugDz8oNcgMqsrwhWEYMx0iUtuCWdJfclK5Sh9RXWIic6rSY2kwFzFWOxvltONdz7Pd7wh5vNx93sO1dj/SISsqk0+yYDvIf++/N9PxLZYeghS1+Yyxh+8ePG1Xw6XBnV2T3uqMGfVHJQzzTPZYGZy0H412A2RtvBLV7K8LHNB8XA7Pz4l1Dgk2dCBFlNgWdkx1wxnpaJ7oKgqdqDx6tBCEwmN35mH8lP3GoQWbibNToFLhX94GR0F+u3mpPs8yZejLckfbttrpka4a20xejYHkDvH2tsTrTPixhO4lYokvVf3C6njbq13nC3VKMa3eDdLZr3JK5VGkNkvvjdzBokrLXsoATQDfYNNvbEuKsdkrn8Z67/fyRqy++5TLhd0R0k7Kcs9UCgVCeSJ7HANBgAbe+iXZ/iJPFsXjzR0BkJOlh044qC9FLtmdJmGBoFgM4vvdQkw7fCSZzYwGCV6JjfsSuMvYjsOEzZ6D8aiHs8dNoY4UCt3f0lBrnFyu2fKAeVsN0j089D84xseWvrehusj9D6EpbEXOGHBY4fkaEtyp3GwEByV09AsKjTwwqwCPE2X37t6aTIq8xIaWG47Uq2K7ClIQISfMiQGx8GzAVINzcZTWkGYEfMlVPqUNBXlxCWnZlOMl7JwVZ25woJc+AKXQnbVhY8+HopdZKSP2DUDctVwlYLbSIDpQU6RZ2VeTO0vigkJWcHJt7eaD++3CHi3tyDfdMGBMxSa6nlNe/obJbT/wl/HzASsHVVxRVRNhh5NwkKJ1EefnhA2en6UPZJn1i6SYEOz647j8P/VhJbk+Z8ftQi0/1hGXnhXxZc8yfnVCysawL3l/Hm7sycFGalT19cxBvWHhuIDW+rjjyzxWWPn6P2HCvCDTyZuAUyTMaAYDOE6WbKqSpEPSg8ppgj2OtLM0N/hF4VtfMB6bCbU6R6o+2IVVItRaR6drZ8PbSiQmby1bvgpRdbiv9r8aWu3D2n41nKpj+5gaFklnAN+NOIgFIRDWTrjLEWK1+Ys9GDs9kKdPXEUJ3ME0+CAsU9YjYTizE95cNVQDncPsJh/xhg2mSLcyM1fDBlQhp8kmF0qiudVFAg8HHFEFOEDuC4i+AHTBG1297LEXZQmX2Gur2kjy7m1Rmgp0gKszs8//lTiB/A8SvtFFdphiOWEaTRHndhbGtu6GqtoTMp7vUN6KrdiUDVrNm19PGSiTu+s/E1ankxpU54zclwx5Fuo2OLxH9JDRz3qcUEWz8hdR+Rz5WHz4y+whS6FmHBLScUgFum0KJ4lpDBfsx/DGfoTqMQQDM/53OCu9fQHqUUi7zw6nTGWBJv4lR6/pVSHltS5vNEH82m78y1A/Zdj3W2HG1Afm3Jwuy3b8wXxMWTRhh6YEZSH0njXmjIjK34UOr72Zj7BEdBpqeV0IU2fIR7TOVYR0KeIGivUQFB9d/KYBcnInzp0ZYg6UwR+lY6Ji8Fxs0eE7yVWChtebpEv3CiTKn6PyN86kxI1lNYP0F97fTu7B9g4JmDDrKXJXdAGMEFa9IeTxznSiQO2KzYmppAGlS65hVMZnqb7Q8Lrio+sGmjgEiczvg5+uWtuC8jupxZ9CVwr7USm4tLx7DoKbt+563JNkqOcb/rzeKAniqjvfFY9doB4IGqI1Id3m8ycqbTXdHXDp1DazxqFMp4LLZys23JcrEng8nDn4Hhg0TShRTzN36OIhPdXchBeQD7asHBkNySehuXdT9WWjS3XozpSGnbm70FHpReynV3y2m+E8Tx3CgP3dELP1IpIjgpU/N4J65ZozKxoJBeM0hRMy5nchuzbLw6BzEtHUlaEOPY+rNweINc+V5g6+LFTErZWXXn3rR35pr7ar1cYL86xp+IDzuWEKyA9y57tghSU44LBewpclIE2nROU6l3OznIJKKC4/K9lVEvam8qsO6jjpUkFa/qjThzyHxDR7Wz9a7y/MHLMf33EMx4n/b/Ny42v0GRFUvG0rIUQLZ+nWwMh+zqBmoIBA3tSRJEMR7mJ/k/oLOo20CvJwRaMzghAjk7nyGlZOC5Hy8Dy04jWcA/C73bFQBVwR1lPgvYYXMpd/SWA/YxJzgjgcWT2nqZJg8r1x+TMB2JjuobvImVjUCzaMmGMyRzMKRvq856XzydEtAexZzOYse6r4weVfHuG87dvqidoF3QFt/P6KpaxQ/EkZnA8FDwjg8YrMy4axlN3TTUI5JIrDqij6R4cpIGzPSik/G9QvtUleC6l0r8xexhTO4QKCX1NMnnQVuOlOlf8nK2aDXTci8c56Q+NgNJO2cYK52WU1xW401iNmXOZX9ZNPtkrUIwXXyOD/Ms8TF4Oy5IyHI1e3jZ430RQWHcZAkroRzAGngeVtTfdhaiPyyvatNGrnGcvt51ctSqLxtzR1P3YSM5hXDeZCwvLfsbYBz0ooOx2EexVkn9P2YOXMQV5ie82mqEPjj39++EYhfnulH8yOQ9+WT8HzvhPG66Lk2znAZkPAKWG7i49kIyIAO0D0vCR0BQvegXwWw7gDt2yKnb9YMd8/HU78rKPkX37U7jWmL/kP1NoYx761GO1iSBYgN6P2yWOh8rIZQyYCgyuHZG7X1mpVuNEVfJLvhyOcO3/nmOP2ckKR7AbobA4FGdmcFkXHJOLFRTvagIESdYdtybJxPqdbshpqCicF2Fz/6jWTNP/G/pXoc4iNkzIOb82iznM/ZrF30Rhzed6qiYZozeIsiRS9/VxYF7iobX9L/N5Xa7p3YeWfTN9djUcGFCLNpqFGoctc8txuM+mGRDedy57kDiQRb29huICuPuhzhexiqq5vfZoslNRJzRF+ZrHSA1NACYLiWj9oYlDJmdyOU36mq9cUchDjWcgstnO32RwdYK8FCncqvARnpLinqIGGyKOxrFXM4VjkdK0O9Y3E3drRvjuoQjFjtXmEmQVu21t/BGdBaSDhaisYxswug3Ncg/O4d5guw19FPvKPkoHQyJlLtBJQhMblWcCVAvJjywo7Nxdv3CFRT52qJ8yOjJgppMatIwbgd3igAuBcZWrfRDUqyG9rRTaiAi89Z700bfRLT2pydmU+Rukr3qpyfmGqufRPtWGzTIv3qHn/j0lgQgUTX7K1TpL/bdCf5kQY6Eu3a28uUS8XRMyD8NmILwCqn5dJXjT7lIaftjTnR2FsKYJdln1lDL8KkZZNAJPArrw9tL1qoABK/dYw2O/GTe70Sc7lZ4noZvd2mfRhQMBwUiuF/HffDSFIxtHOe9/IIhhCfQL/sdF8bHphdf+mjhVdJkFOezSDlt40GHwevXkEQv3FK6txMgsDLV17/lPh9yccrLA9FTnDYnjEFwZJnc7JrVhekdU6CqLUyXBbiCh7gomJZn1hcTdFmy3J+JNmqyDWxcwyMadvVUaCcW7BJU4VtGDsWi0QDb/dWc5hNqFe93de6jhFwAHFz1ewtIkDcZNcjEEu2xBpvnys2lOPuRp22xqKaFOEcfCkIurw62X+/jiYJ6B/e3YjNVq2KMatU2VE42bI9dnWTFS/oDL6pkZqSN/NcH5esVMkMjzsE98FbPk5HH3R5eZbdh2xzxo4NF9qQMy5bsfj3F912kBVD2FwR3S9RPpixM8VHygJEYTuWoY7IhMkAD1vanMH0HGqhtkI/IneDQdXEzh7UE3pG71/Vo9pf1e4cgva/HdnevwnkN9iZJHwcaHCwsIm7eUTZC5gXR76fky762SWJrn/JsOmduLE2rSXQE7mnFEHzAg9LsPqYXlqGBQDMSyph1s6lhcbJtdKbucsHskif/4L4edaUkA+apoIjR0kEUuGJOOe+QDyIy2f71quIIKy9By4GktHIagcMn6GHX574/g2fqxMw76O7q88ZfDZbSN8/B60UjRb3cCari2fl+0/mFltVuB8hvWDcBoCsl55TgjfXgnG830h45pY2sVwcVPbfGq/8Y4ZPdHsUyHFqi1N5o7qNdcl5eUl5wXPFsqiYlfZbAAnL0FIUx4+E+uDBIofyGtdgWymk24r9zb7BnwUY3GuuA3jcrcNFPQtvSSmts75x9yDZecOFrTiWaHQrDw2fnQ8SrgLpD8+lnBEKmX82+osYAtaeXzJMrnwjd1LqUiNL+icPXKpjyWHN7zPYvW1YtobdR9REaEKckDTY9qurGiVE3b5CRQ9OrDrPLf/rlfCAgXpH1MxdSxmCsikJtv2jhSCrnTJbIKrt+GGDnYBqqthQf7X/trzamBBcDAJ5IAfwrlasoSPyKx1v6aO8uA0X/bitknXRPUTNP4xtQ34jFUJ7BDS4/i5hDLfygz9eAURyfqTMDKZSZr32g1PBfHWuv4sx1tGKuNqRpqMStNFbfSFZ2v/Y5HyVmyNTBlCh9STNGbDohxw/YF/6Hn5IGR4aPFIBoIHuuJI+WNVAhT75dWi6ojQYv7cGRB/ssPxkfjJ8JrTD3OGD08+ctXCU6524NFPVNUG7BWKUUnlOsP4mLawpIrfnF1ai3yp6Sybin4ynMF4jxd0+n+wUHCaA0p+rJhdjv0yfRCV5ry3fqBg2rkD2wGyh4Ge911UxarLTKnD5EagUYGNZ1X61vJAzBU0JbbxdEwtg1lr4qM+4L7KiWm76FzoEclrk7ZnTVEujJLXQDTLqnUIKNDm4Y4LOC9IEYXBBJ2q60ewCIdtDUHxJMAj053Ldrcf0DugFyk1C9ysTD9+JF1aLGmrU2hydlEzW/AT5nHs2zUU43+OcvVUtA2e0wACR0RpuL6z68xSCEWfjYJdF+YIw8R4Z+KkWat+HXj8yynza0wmJBIccFqhI9/uWzOzX+OQczMk8WU+UyutdE8/ISXYC1x2ZYcE0OXGy7eKMl5CYWFV5puI0P6uELgG6mbyzF2u8P6LCuEnsPCjKaHBtJQrnr1zYEQkZig8Ot6aiO75dea+tqCOj/VhVosW1V5irTznvrpZUEwD8/dXEd9m9xOursgOQuNKgHsaCi7uwlSXoPbJ97nyZBQPQuc8AZhFhjuv/CURh5XXe7IMyn+wgxxIOgUiQzeYJ+xISX0fbd+e3KJ659BC8V+gdy11b1F8RzfE38g2T/Sn7gRmu1oUAzAfO228aWEwIT3OrB7KMHjJP6xhs6AslTq6NUwc11mAE3DOFbgnry1xmg94qpDWnNnjQab7nECFiRnRpykpbAgLzC20dsHrtt2/Hgb0qOqRVT/y1sSzI5WchaXGGD1o5MKI6aEFdcG4/tq9AW0Y29ffb0WlylviPY30063R1x8bLF6A/Ac9Ve0mXTmIWAs4ZRNd1mRlBCQe9MgncTI8Ly7uvx3BJeVZlyzFHpXQw4PX2SzzJkX7lSJ2s9osvTDAF+jxFgEPRD0wmYWSZVlwl7MM5X2xkLF0X/xelH5y+3VrWKs7H23XsGBjwjd7PABO5CZTa7MuzVKOZA+wz+fZOzi0y0KufK5cr8vVcbKBULtZwn3kvDUFM8GycQp8/R7nAyYQ+S1BUyr5lIKC5ySCuMr/fay+VKm9OdG3Sz/jrmIf8f765YZTEhrd/G5Q3JnmSXV0t0RgLmb5iF1aw/Kvqb784i77+P15jn/2pj98wW4C3C3jLh3ZF6Pph9k8YAkSfBxOjPAL+lklvNH0DTuxPH3vc5VxTSvcLU8IAYNqDyRZE29np8kT2pm+yg5/aeo2t0g/eDBz2mJtRW8a7P4U2Pb5+eIDeeCuhh0MM1J0r6yXiGW+usUcKpEqWPiWhG1Wv5YIPeD4heoCj4W9OPb0IOupZJt0yzPrS2U/xtBzk/nmrT9iMila2yVWFro6+/4ZVjQFEnxjRwDZNcupJ9B6I87FPmq/v8MqqbNvH7SHyNPLQk6ijLuqvURqx9GcwD8yKYftXIXqN8jjM5H+ymsx1FzdUPpVdVx6NJSbLPoZcd3pfDelpsxqPtrrH6CCtI1td1aaqdoomCq9l1TSFHexIID+56oiyC+5tuR4DGPacydaUn5pWeM2KZcXhnkigQdnJbo8ayi4rw7cSOwQ5NAvOR4pH8Wdm9C5u8R8i1Dcg9ih1pvLhq70ooPqj2RycOlYBUdPNWyHmr62GkEqxeIvWS0MUCc2yp+TSq37Py2O+7vAbSHVJ964Hls0ZgpjKYVz3VIzqICOmwraPSetm5u5HG4Hudg0NWO93j7Sff8I9oU9Gh7FASYmNCCrN09oNy3YaQ4TFSA3JEeW5/XDvA3i54HdmtI4GxXA21QQsgWwPKvp3GP+EDO0f1yASUtix+TYGjLOD+Jj8kuVD1+qRaNqvSON/8qSRZZd4e4eiDwd4RcAIgrmqjqcnjJl6ikR2IuxhBHb36enbIoBB3ismPFf10tyPScZKd+xkqmT3IFR3Q8Nleft1LSS+61pTPVKN2MgkCe1cHvLjlGDgORUVsv/Dw0FYZ0QFN9CTs9HFn9bZ8YkBIPqf6DdkGTVvgZzelLQQpeETq7NhF2tlzn1tDmTLPDOBk/7k3xJSUARXBfmyxTk4P7HxT/dcy1Y2lT0mcwp2w2PHz7NoKuRWIYRKHqwXLtmNNxvx9b25CGqUQ9Ae+wQJx6xoQYFgIjfGkpHBsrEmYu/Xvekq0J62ojhIqp3waG7Q3Hq2+sX5eZ0goc8/6gZcbvjVgDXqzvE+THIAM4HiEHoXEtENl82/Bk/f2Dssnfmcv+mdZ4iXcUoqNk7ogp7EclJvFyEcqtG5Dsc5tcg8dAwdp2IT64LQw8zsSDf8aYnKGMl6AikFup9c4W8G4kJ+4UjU3fOpBuc4JlOc90VHTRJVhsHjFCg1XOSNNs+DWzdKfQtV/AelyK9kGXx93WodW2BHb2jX8rvOO7yQDVvcq11Z1PXkkLw4RiIB4WHZzGxRTBWxOE6SqYOYW14wcp5jjfXz32Cg1Fk1V6DVbsjAYAtHmlUtswMG2dco8x3/2ILGth9zrYI4I5YRtG/qIhG7n2LaKe1avbybdaY/23sHpTqTLYnH8LtS/91jlBgP4Z4u1LUd+VRnNh/3v1RuEJvr/SeySDm5I35f76qw64Svb/u6Zv9SMUz3IWvlu0H0P8SL3XC5KGq26OlFBdzuG1XUvdS/8G9d7rWp05f7ho7icYGpx9iVZIIWm9ryBr0oPUfJ46fK6JBGzPiYR6FJa9UQ3Rrv1Hjw9pHqAeyYaiv02dBHI3zwBszfWiM0RzD4i5fN05HkHHTY8hRQEfF80jQISLmzgMKA/EgQY4kmRZffUWUlsP0ZXD15ZNEQjtABkxBV5VzZpPYu7kK4D7meAXyhsRfJL+OAJ6aaDQiWHlYdyzuUePd7bIpL8wfXhwFfgXilSMCPC7bnHAfOVMEWAhivTbN32UFidkZ+2gwQduLGx0YcRO5JNXxE518POe4n4L5EtuwVnOE/LQpsA0Zw42ivNSjAt3/D3QIMRXc4UK7TDdseg8oX7igGcLkW12JMDudYTWCt4UDUtUJWqO2YnTxmZNpO96wzIIYx9cvDrjKkqU9TMb24fOeHWsk/kYamQZwPIRRtdFczgjdhUj3U/r2QAjUlB1DinTKTjqncgerYQbA6qTMNgD/vTe9plCSorm4l7Tj5s2/PqTXbks/0rlH38KweeMaadnlIxdOz6w4JYAbNmEU6vleYgzGFOI3wUJA9ebWQAYksNh3l4V2LY13qItW1jAQGK4yoRZOV/tus/bSWntJ+8+GNq+lxcYLpJlwsr60g8+3eqOiUU5K+jcgS/FZtcOA/Sx9mO+cMQjJqaBrf5HTNeewk5q7yJLU3fH5s/bnaCtBaeZoRld8QTaEw6FBYllelH0jfXPObwXmVIq2+d8f6gViZ2JIVctHOSanwjxo9a5OPCoqrP40T9gERIBqziTO4hZuBJI4UZ4uxn7Q6OGZTm5NmUqmc3SlJWW3NVZXa7rDl/x++lQ+oXvCxLN6Uj3hvmUxlBxG/6NMFChiSihS4jkAZ13BIxkCwmcgTKQNYu7qt7JeZPXUtjsYpkSAC9EWzlVI/kNSkNJVmLrBwjs6Rscrjt4MNUlUoWGpRj0RKzDdQRMjrKWQ6D7CNKjZVMEv2JbnySsMWYP72q0nSY0YUNKU/q2FS3vCYN00wKsnSx7y74fXnCoZe6vzLyyG2/QRjAv/1u1n/ppHU/AIhJz4/ZvRBtR03yHH4digOMcr25O32L5mGaAyfb6VDdr8eSRw2hJLMqIPG8l+d/Si8J9eHDGAXftfbWoWaJxJ9wDEDQgeAlRvewK5yzi/1lXu4tX/02oMd8CTgFjnoJnB0Yq87HE6NLLWk/cjAs33PQPMudfJ7A2sfcgeVVK/4dHcd3hGP2ROUdiF5LfaBqDxDbJj1ledaowW7jCJRyd1R5xBMPHKEmdM9he60nb5p+dstNqOgCM6UXsxZM6yYw97ZJrSLJRwpdmOKoJ3IrksmVzKTxutdUackfRdcxFvl8U1E5KSSGpszHGFv504ZwN6yNA9IfkF3N3zrtET/Gc1ahdsBW1rGzwaE5pPzXUrXu6w4nvnCADScYDMGieujkpAmkTVhYZP4nGgLJrZ8e8iN30455LpgTYVLLffIgJHqhfyooYa1oC6fz3aB7/tgkcH7E3iyR0OenKw8yvHWugAP2evwbb9bpoa3N1XvPopcUyzhdghUvZRLtEdTpnPL6zYChl61oHDSVqJOpre6gcCdKMqEBcE/qI9N0nrYc91+kL2Y0VVfvZc/lqNOsh41SgPsJ8L0pfYG6nunShs/1b8OBnYdVwX6yE/4W+QGIuT+2mTuqnHGpsvV7V+uw9SDbSV/3W1BpVpjk3NzVSa5ggwi+Bl2ApVrvLl0iGM1ECVjkIX/RjDkFKQwOdzoFK3OUACLMTfzGBwNQB2ozoBTD+dK/SEGg1a3d1zgl/BdK50iFrBKvbKd9hmeVkbcKGPOfyG0hP9t9KhB8904w7kFLtqT3XiVwJwz10le6dTcnlxX6My+gkl1Vv/3olKORfxBb0oB5C0gOCvPohPMEvMSNknvTNKBRdmKCpgQ8oM0iOkFeOxKtfdlZVXPJW5JU5D4UaDmPSMzmqYFhZ/SJFrg5GpIbNRHFI/YozaT54qzFSzwD/A6+2bIPY2v3HAasz/RUqJ4CWouhFtqY9RBMkosQX7NFVG+rYzqdWhxh6/g1PgN2njn0UbmHDmBQiqidakQT/fKKIkuYhqw9f48lrKfbqqixmBOu5uFK8ZJE8tqGqmIsc4UI22h1p+pAisP0Bep5dka9HE9bX0X7A1WVer5dBb+mFz1bLXH1rR+2W7t0/r0KeBQ1XRZEO/c7jvsRP63NJBbRg8IKQI5AxdUVlslnMbzCfIGaooWRaNyh16Wsc4QRPWzXfiKbl4Y+lkWLsz1TbI+flLy6lB3iFZA4NMTTzkhY1DamInbcqz08OkyEeLASwc3UyafTudnBGmnBXutqylDYAapDO0myioDkjjHYeQ5sJx37HTfy2RT7ddwSer8vmMzk59s2o4Rtb68bAz93iuzh66SBF/9kCs1rjP9gxHxGW5cfXSaZCTMCERdHtiOTKb2EC0fPROEMjp0qvU0yPb2MOSzGW6FF4wAvZk74vrvukBb+v+Megidk0LSIndzocANPf+ADrHPB0Z4K6/9rCMc3tyNBEKuHI7Y5CYbH91WOY8/tJWTYdm4p3I8ISliDKOSvUN6YmPR0DEegwcggD1OCMSYdMwHzeY0cjglwYKyZdBlb7XDILilyB1HyDQ5JiZRs2Dh5FKBMzRQlQVBM/MFwgkbaS/82hgEfJyhEGFRmGgY0+9FdJYlC4ITM7GUAdkYxRR62Sw1aGYUDDWPvTFgUERbMygDUXcG3hAH+ro+hwJKE0Mre7cZ0FWqEztWjrtQFdioiLxdQDdBSzf+6utsFCsbxLKll9LR60z7cxrkA2CRPVzzmLZU3APCLO0mmucUXd1mwc2EDE1JvAHs6n7x5HTK0gbKyJs4jCRBj9wP7weeiii2G3zfR1XWX/RF2juekt3In6SOkUoX3sZCnMQyjLteUfqsHSq0mT1HSA35EmGQYv7Dxu7BhgSFN6JGAtncjd1c63zmfps9ZCEeSUWVLrSSzlyjrSPtop5B0ZnB1tXJr+lJNbE8tWM6pvnjwkO06710goU2T2a9JDOxdVd/mmEiRf1S6tF9bRPwS5kKBCwP9M6jfy3xUpNmzIE6bFZ4qB6a1ZADYw5YNz9s9r0OndA+f5BZUlMmWjNqqmduJO7zgZ+Ssd3YmoIdnoMHlClqA4827xPlCWMBNGV0t/V/7dBvQX4u1Gnva0G8AzWRWkHv1ZtNTT23kd5+38F6ejfYkZLYK+ZDVKs1bgI+saYxD+FmkEyecwrho7cdzwUfv/K9K9H+KrQQ8251bs2fBj70HbNXMRzpfAgCVGTEMYgaz4vMFCFtrXuf/wW1KneIIi2X5/4Xwkm0L3q5bRrjbsmPcCjLvvaWEo9jBu9X56CFNM8INcWcFMs3zqr02cnqRdWivibb8eSzOzxNlJbUy6XrAG2TC92/n1Po7lYaAEKNovoym/hPtoXUUsFPJ9SsQs8L7T2dxIAt3cUnIp3UH5K6yJrpie9CtNNPWaG0j5A8ZUmJdLJll0wzWl/qcIl2dGeeY4ymLHt47QNwWRX1V6i2T9NERPerHX98NqC2EFhbuTAbOZXMlVd2IoYvtSDQivL+9e60TFqhyVDAFIC2d2rjrVY5dAQPpdBOfgejtu7AhgS9iqKvXZpgzKdrSgEaxI96msCIS1u1u2mgF3zuT9toAAh173x5sL3isJ1N/tTfg26yoDB7jx8FGLcVa2VTn6qAElbydgfAQtQtV+hZJOPgWBSAXUWRiUzHM4qGNXg6DmbTsKXdBbXOdo/6NCV0K2LXxarCCMH0WuzpIRElB0ctcU1mGGoF3N0Ef90kTqySc756iw1gcUk8W3nljADS6GrDRGzmtb1qAR5oJlxv3anJZ2pqfARIng7+UuxXKIqtPBfA/vUnAwUwZLoO3cLOSK2wrj+Zp0wAYnqwb2qAX3lyKKXsAZNheoF371IyDHbHz5VL8yPMvBwhODW5gUw0u/7VEPBu6QJlUSKsfXXuvO6IIcZ/xNXhtWRA1CM2RZE2edOXjOnp7ivDcBzRRh/QI0nknVlfBsLZEROH7IUkupYlp33t1EITNQT2Zpz36BQu1/CiCVC1olxi5ts3vliWYN+7OZFR/+xqCZ4xe10woZpBtHa6B9IXhjHT9pEWU84RYXfgSa6iHu6P16z8mwo7Ont2YxJ1CGmzgnu05lGHV+NMR9YU0uTEO8EUZu0B3GbOBVQay8hj+o2RTEOvhxEE9zqKYFrTLZIqdWwne3nEXYYuR4FGBWdDG04CgZ5vPzPhMXFQmOPDohkF1ny9KXBD61VpISWniI5ooO6MsRGBpJUmQbP9fl2pI5V0Mr1oEP6YavMdOLTXtWtj+EJOUkYrijTtW+Dgx+QPKp5Y7Z7lAbIrHe/SZyfe+VwN7X1VnvKCbf+mYskH9WtikPACr3W3kAMVZbEhxQYbCIcFi1G3ryQBdFbFZTD0PLqSosqR2zz9J+LvKJWnGq6nSPZ+yk795eJAz3WD2hdELDplgd3p9qcQAQ3oXWZR+GyS9FP3zpM/l83kcEDvXK3E0NgcfD5dbViTsFQxToUhr1psHQjGdFd2CeUqWFubwRzpOS3jg8MKnu1l2INGIzSvv4x9dodjQR1v9SikTiutBMm10a82eeDBU8ML0xwPxgePCwuYGpkGiHv92wcer2ER2l82EAdVhRLftalMXRpvoHD8wPYcwt3Swk8U+CgGAPJISodqcQUlvCMMeE5GVSFGs21rh7Q/k8AeqQuKGvDdOixfT9CmhWh/gv4f9dsT3jnX0DjKeRL6Mo10ndmzjgjA85kl5zY2Al8mn8TC2vQsJFlIPpMiooaDT3MLLCXMMKZIBkn8qX0mZw2MgEj0noM45c/4w4pkwpfzyLc2Iu8T2CsHD9nSK0+3Pk6ei4BoJEbh2c7TdjQqcCF3TOiE5sUDeEOmOK4ArQx2QOQ1mhVP+XXHQ4/4DgHz8QFMK0kpsba2o5Zob9AobYJhnwjGRXM+vpR0+aA1vvzzqUvdVoM+uiR5H8/nTV7Kh1voPmsRlT8SolOIQZ/p3zLO8PXnrmhwMHJP4TM5Ad2S8BlYq50/4fdijRBRby11w3k8r7LbM8TguSMYpuTsqs8448YVT5uFZ0EyFdBO/NX5R1FzEBS13uuyaKqkMHbuyu+umE5Yo5OWIemUQ7MjI+oZBEai1HGNHorAobDgJyDK0ByCR7M0DHaDKdFGYT3qf0P+6rtP0lv2NEefX9S63dOTNDvBJXzR9hvUGs0GpohCzec+W+YQ85gYs+VMf7sYcssyO1ss8WO27jFTK+etJmGa6UmGxTpjpka7YWTo0CvF3JkKqLutjchxMD+XuShjq9IsHAgaguyBRnVUQ1D2fMDJrP5kQKY5Jm/opFS0E1KbAaLv0dm0GwsxVrLblZM/YL8W/I/AIrIdJ/yEwr1rfO1WZNwaNG9X9/40bOfJG/euUA8jY9kuODxsvq3TgLoGyimRaqMpzbYoP/wCjU1FJ4wkJoGgLfhloYXJ1mtnclMrP1kxxMAVtj5NT190HLlt7IHWlQ1jiDr2Poj7XuzVgzt9BKs665MewJAmEpJbKsJcUPZ/XOPGCNc8LiaUnvZg9yAf9jLRUXZ7nmQjb+sSYY4RiygCKL2mMY7mBnJzlQG+PcW21GmkUrW4RQpm1c7I45cot/vXwnsHKL0isPfKoDb1dOEMi6XfU6GkQHi/Qsyz2179f22rH0gAS3t8ePJIEXkxXzCmJotdlpbZ9j5WnxkUXTq78cKL/JBsQkQ77CAbe+X9RiMIb9a2ieleA00Hgkj0Ep6NCiSO54hPafQeZMZIOhqgjhFKGGfsWURREudzXQJ2gx0H7o7tmPSaonXogTnu2sUTrH9/W4xIMvmP3o9BOoKGDJWFjXO5u6gyWmqqNf+SoEFGr9f1G7qYP/X0aSBL6PqoS3UirOeWvOxL+3qMAvAPmG9hUJ95KLNo9PQs4R9usOSSPyYGJ1+fS3WuBXXy7oD9wlf80GYXL/wiVuU+6VizxMvIiqRLC/K+GW+Tm5++KjhzhIs87EjsUUCYHvoBwmR/Ss0fVWq2fD4xu0mgWqqJHofiY4LiJcXWwK13yVgaXetCA+pUZ4JMP3HolrHSuaL4CtjoJoSaYeM8iAp3+CzXjBtR/V1GuJYHgLONEehXAo0L0Z7ipFv0cQbGB5ss8HbLlaampla0EOd0PVirgF5KFO19SOb3LQPj0LVJPVspkWGYvy0V3Hn6Uo1EF8qdH6yktcNhXLF7ehv0Td+Pjy6uv9A79XK/OoEI5lvDXWX3JWgfEBeXufeZc30Lh+Mz794HJD5Z8nlaMRWPSofNMzOTN1q7pBk/+0iHyi5dwO/t1dtVRlW/FpAkfOIiRmVRefrqraRaD8bqBeFNRmA3G9WwLwJir5pkIrcSnQcKbqqnx3gC+Z3rjJCW4brbBeyiqREhohWznSSNWWAcTH8nQEJB/6+XU0f9mHG/Wir4tDXDMOSf2hcWBzz8F286Jy+VFuaqSQ8jpGk61Tv3YpwjyhLIv07YIwol+LYnIt0cQHH0ySeU/ymi3tZGI3394S2+LioZtoAqf+ZvOfFogrfUA25e+Dmxz6PPEtIvpNpmP5Rbh6KGooommdujSBeve6Z+Bk+0l3t9TxXP0ZhcsE7ov7XzktJ1VZnc+HGQeDLp/XtuOV6VFL/0ZVmz+bnOm3MzcEXRtVy3ywg+TOsOdzJNwXIP34t2Rz8mwNQgvfH8L6l577TEbiaqYVMcr5jCvkgDiF/JUxXqFRFyfWlxVOKd8AR1PzZXLh9GSYbv85uxAjSSIuBp5H91dbBdE8c+BHchfZhJDSIxBX/Izx9P5vmxABTH85F6Trx9MlKUlT9JYczm0kZGY7l3eI99njj4ePOnkfuKumHU64rSe38nQ6awbKOUPIRyBo9H7eOgZ7h3lqbHEdMaO0OmDovXgw+4y00/NyNAM8j4nScWztlGP4XySzuV7d8BmiUg8eC5syRR+xA9O3hILausetAebfAgg8b1Z5r4Rl4Rmqv/BEje486MkXFoCMzj3Atkl4Bnvnl+fE0Y3ggk2HTd3a/J4VC9LWH1Kyi5NcH4suJeojPa5sCvmZNjNeWudaUxG+Lnnc2N45msq4lVfkk2lWpurctyNLyaCLUrYiG80HmxBZaU1Ukq68ideUgdzqmQsR9uRfIWPSr2+zCQIgZppr8l5u0QyCLte0CwyxKvfAt8jLBmeEr7brNE8YDOTX0hCaGeyogOWEIbstMTcAIsPEbmTGCOT/GOphHTIy6ZMn5gESI8L8Zhz3eh5GNaQlNoMv10QLVK1zMmE9ujYoriuNA8YvVsKDynHxvZqQ+lJPAJsIVhTTrpCqKURzjlAVRYhNlrkeWo5WOX6ER12yXq5EslGSRHWIeOGfb2ZC9seY3r7RqENN3dECpDAMHF5qrSGzRpr6S8fjei2gRkp00sDbt+euGC6Lrh17LHd/O2owW/ffL03Lgo2Ys67iQOUHKh4eM6qp/A602COeFGluqN6u178MR0pS62bwtMfXSyAuD/LDx8kfcDqnUwGWrYidTH87DxnwNCgtVmvNpGNmw8wgLFEZP69iXa6wyAnuixC82VM3olJVPHyZrLzZSP3yuDHOcCk2z1t+0AvOzROA7uSf1Xfug8KTxEB04yMu6qJIC4cirPZ5nDjU0W/s3FPaA7Wp/EvCEV5k8Vs/s5GV3namw42LkpCfEU28osCuonZKGspdvOoKzQpFuIRN4NcWIiLvemOTyHfIFabTSJga/G/JTMaeRP6z/SOR8J0MpCM64NAVm+K2bTk65fJLh5aaX4dguKe1NKf3Ih3CHdzSMUJRq85rbC6tUYPfDM1PB+dz6vzdLIk8cfhuiPg2xFlRtAkeHZVl0vXdgrv40knPioQXz7iS6lDgii3X4Zm2sPNe+Y2825UGyK4BKKHm3YmuCEEoM0qcf2cJGkLcAoQD/uTqb9nbJNvLvg79ITMvH/GJBj6hxblA80u/c3Nk77rRsMpsZRaJ2LCmVcnMaLd/L9IgHuMDYjqNDzyxsHtvykilY7cwFz072U8PEWenM9+Zd5UPAjamZ3L1cmO6TzifG27/eGNhRaut+P5o+K6mrSmpxp6L6KlgU25/XQ5GiEqj3dzm2Ew+jIvKolJHSuLxoyTIA5EFbdpRp/PIyaqwyZzcmSzKBrrRUPbXKTS0h4rnKMc+q6AlQ/UL/RScYuyNJ8REkQyoRaJUcgKTF9A5GK/XSGWiF33axg2eFKC67sKKG3plsxX7P3gEAkXch9Gz1C1QiD40E0EhP/FPzeVYKbkFRxryuxccvVqdkH2OCqJBL9GLm0rkoaKOUmV9szZmuEP8zW57+gg0d4CUw8vRzj2a5tNzsembRSPALsKHgk2VxoQe6LOygctY6/10FF7V5Twiabfh9p4wnm5QJv11h3V3VpEPcR/4pNhRD31x3pUgQtKipCKisNUxj0m5MhMUvDyQaqDjpvUapIqhKnCC6uuONSneQa4KfN18cqRJkMT4uhDCK5lkXMpsekGArcEKaPfnT9qGiXKD6bn4M+pAIkXbdcXxFzcpGbBdvpIrY8+HlYBXjA9+3zxzmvgFxylRkFaChK+nLHPNwkRVSmWPNI/G3cXeualq+/EW4vLjiuD+JUycKw9HsNfDMQKP0c+72Ccy+EXdDsJXXwPeBmMWR5uvOcTQ1JVoIGrr7J37eJgaqJeq6htVqJawgQSzKQl0K7KrbfFdvJcPZwctunCcfKxIlKM/CeLf92StPBQ3n+0yi84u6qE7R885Fuh4FwhDoS5Rx2I69SY6cY5HDueEuWRkca1Ag5JQTYoFme2Xex3yr4jWhxLU2FvlxqaVMEiJy5iyPHj5ykrkNW8ueYjg7AWAEXbCjwN+7KDPQBwJLPYrbMBBwE1YTN1TiBlOcfLSbP/8dZtSvdqn0SlhY/bej5OTnEuZHCXdJkoWIbZkH/pkbp1RoCjY+3p37D6hihrRlq/Gsd0WBaL1Ei8jmgbQ4WJH8jCDh36XOUcPkXQdVWWIepRKF1DGy+BbQN36SHQQ+ARevnOvr3TwzoTqr8GUguyXoOxMmnRM4ZlFNAUNVzp9FW0lU51W2IkoGKrA5NaMbsUtL+2PnaJ30AnoWQq6KS4nPT6N73LNqZIYYOY9SG6+ZWdb1ymR+6sdu+Du6bVv8JOJMKZPyamMDYNJZMvZ7+ELxuFBrnWxnyAEJUt4+QknwAxBPIAFXxU/iLh6l+UzAJ1INfVdnw6WXwJAF7gffbCr6pfK7OJVt+jF7CciCRapPuJYMusD8Igx7styFCW/3sBgs/xHNtInv6vjgXwAdfn769/RMJAGGq+uy1HjUOEw4u8ycPuPbzBpmWDSYy++FMsQ62cbBTsaRAPaYpwQbUbWDY2LSJa0gyVz24AvzRWnfvd+fNF53LNemL7SxYz98nUuotD00sQYYZ8GL9f5YXiBg6ciwGaUPTqUg7YiH9HuSuWePigZ13d7GTzInLZImRWJE/JSnMn5VOhgoNURMpNW9FEbFZEqs989lXA3FR2KIB5pCB97b/1mw2r3tMOcSnCr7miFQDN/snUbF1pG50Cl2aLrXQXdcfQKtGh+eBA1AjYv8UlnbKPXCI1B4L9oJIAlhKRXmmxOj0SlKvYZ1QmEV5VTqpeUmfhnxK//AO3GiT5rb7+HaJQ1+dZqS66lSJfHJSnsqq+rWjJoPZwhHIG67Dro2enPlqZkFpX8Hpl/IIOjUQD8t1CeMt5BVMDW/mcFXvollPz60s+WPpfOEu4BVB0Ck+3+rfcizxrbUt3k/aN1eHpjRfN0jrXQqYGPIZfDmfYuGOHL074YJ++Pr7HheAJqzUUZdaxsoHLPNIfrUcKL9mvG8xyM+nvQq/ZOI3DX/WcvVwBUgBAHRFfVyYJh3FnDWdtMvurfiZva8RA1zxFOtvMZiPp5yHmpspSaFiWpLBF3KbgLeoMnGynZUwdMadrgJdHmz2aH1lMgL6sNzPC0xsQnUzjnp0FUcujI/nAi9ZHPdn4pAsacvsC9yinQN8lK9gWEEo94V5fwF/rMMrD3yrtoV6MxDcgrsJa6RSVmKm5nOhlULrnrlyYzS5XERRhOHO0POn7KYtWGLAigs8R5qp6p8/K9SqieK7alBCkSxD0ibF9h1yY6yEj3VHIdfBSBiyPzJm94clRgAN6IdzoeYeqwe/J4kssslBO+wUx1jH7mUjVQl0sMG2WiQOQnIF+pRT2N35hBkMb2Tu/FDG4wFaH/Zas2MXhS2Qinn4WAb1U+t9zUMMN6ma+LrwWO4VCpqowgKdKluVvBSJoTf1/si09oqHxYpixuZcwJ/AEUM+Se5OqymcjzuyhONBa8UOQnwfcaFXiaITdlhBYSQvfZI//zwBfWD+VC7sGlCvfOb7BgfYgjextpthBwVrKxf696SCFAxNu8HwS5tmdG5V08LyUrLO5nBn0VsA5QjVz4MEE=\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" normalize","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I���m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" to","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" replace","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" hard","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"-coded","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" values","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" migrate","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" usages","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":",","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages,"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" and","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" real","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and real"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and real"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":"ign","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" UI","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" with","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" the","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" design","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":" system","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_delta","contentIndex":1,"delta":".","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"LElKq9fY8CyU9HssDOv33EsHiFJrUknrBQonL6f33JXXDPb1TUnwO+uhz+quQp+kdfc4tm4gQRMxLoG7k8tHFAtj/17/bnj45NO8JAGgtUneVlpSpCQopg4Awtn8FC5hP52gqkFuIHosXKicHP3eyGx8bX7IdRj37bQouPqOtPpORVozRhJD469V4b1/cTFpss5gEMB/zRShsBrcMS/ooPtEkZnk8eMxrmgpQD/SzX2LkULVrMkr9EKvjwLhaxxYHngnkA5d40MPu01dsafCGymOOwTyDuVo40N3rs9LmowVAR08RR+OjnaqKv+yoQtAi6Dj2vyXMDWF0OBXsqDo2MDAsb4OEETRN+IaKFNPoRc55WxwxL1SL0Wucvd2dsSAB70pZmPp7qMy9VP8oLCR1S21RwrJxeHQdgw/7bDfJNijr1NVzkDrBhI1K5RpZl++Z1/fTP3GqH01ESV/XN88rXjmtZ6XJGhteRr/t5B66Qe4BC7oQKl+LvIeUUKFUX0nIJJkMmjuUXl2/uBvz0mdlipbCLxfNmbAkxge3sSsLZ9C8vtPx/lC1GL08TGu/BhEB1ru5VeuuTVlH+8fWfuhukr8iAE6wKlPjuyBMyp7vS6+wvU+Dadz/OBJIraI1G6mNV6ze12SIDgRQu5XAWdhdhLnkDc3lVFkCn09bMGyGwzkSRFpfcCj14iqZ+WW/RgA4FymCxQyVnKL7jl81LvKIVC9ChMBU8pt/jzD5gqe2CsE4wB9+dZUscXg1yAvZo/e27TdNYDSyUwiG589ja8plRtqGxImAIQruQh6pZ558RKSqAbw1KYsJqLiP7kxpu/FlTbqTzSRdpXiPeWSSJGTfrW3HqbDaRE2KY6T4Y43lEKB3tG+cQKgHx7Cl1V1fH7YieRcQoBbGQvuhATha9S0NQnztmcgaCKM88v9Sys7Z1KmteatGeHNZ/qwhMfUjWQPCAQedTskRIwd7UpqcqMq8L1vL2DYquRZ+z5urQiC6YISJtSBVqH7thMlQPnp6rao0Ffw6Si/qFtfZMo7lrDWHtKpUz+M/4HztXUr4hieN1gI7XtsTikDpQQVOYG5Xj+s0BoHElJk9WXZlilBB5JvQpoTjS1zyA2klMtQukSxnCIiLfN5PxkKl2LLYItfFnpfQ21c6x68adaXsxQw3/2UUNLghUEFFtg+LRpdI8nG5JF3d+lhFTpx0VVaqeGUhldGRvkq14KATbpn1opnnJ6a64IYOJP0Or23ao9qDY5jnnXexJUtimOeTWz0xT1y3Q4d0IiV8TLC6lkWnueRgnoO3n45JIQQHQpzn7pe9w9rzoMQvo3rlo8veNsbDTRmFiNm0GMOWwdGlR08qzxYl0EvNfavTGtXxYDqPf4N7GITN2jj+6BCk144gH+z1HlklevNkQqhsguHreqe19x3j7X6KlK4GdP5WQujHTiixnVZtB7mx+b4Hdka0C8fHDl6Py8zzkQTvYvs064yF7H7fCPlXrfuFMwczM8VGVa4ayElG37/tmQwnl0jICE6ZJLXJS1Rkbfg3SkPl1Re3vZiVSRCpjFcETjkBzsQzeUyLSHTHy8YvH4pkxyM84jVFco0heyJpOzlP0gI039n4NZQSWvG4xpsDT+VORtZN4hbZZQ1IrXiaeOKg0quEBw9gcYmywDDhfpnrIr8Iwne+FueTXD6vXCLCQ6FwufmpUa8V4jMARnobO6gC+/ABxGY/TcSVspozjBg/98mcZRsSkfMNIvKdqtIxeo+YnSNbsSHIGKBmngRusPrs6w4aackcY+gtapGI8vW7a6DSeigr3lB9sPkrk6CnonnT672s19T7veHQETWfb5O9Lp9CP2EG7qFjDIrd6N04k2OLNTCD5k4dwNxSG17IjU8FxSKT4Ffc/Q8jn4P+GFkj7+81+dWZuy0Cl/qJz3pliVxOuAxMU/4zmUL7zdaAFGhQnw5hqfKyVq5rjY12bUbf0oN9ZFv712j4KkxgMTKQ7hDw7ell9xCGCuA5KVpcVSfT4Rwzr5q1mgy0Jgxw4E//KMKjn9qqe/5sXbzIQkdbzy0wEXZBqbHKwtjAx+sxzWDtANGZdAu2QmE8BuhDQSBqot/R3QRzQzx5RuixrePld1lWflWgkaBwLvKcmP9UdnbXPckL2a2+/LFecgksCvVooN8zyiH1W5YXiRWVKH9TE5x2cI2X7SeMRshGUzZV9yfLR/RKMLTe329gsrWLsCcGjxzFpywHhlt7FAIWoiiRCtMU4arzj4kJjKz26/KKCGCEx1OgYD4GqY7IDKOriMOZuSw1XfXrhxKtU4ddfqXI+3gMxSS4Y03mc3yvonwJxzd1qXk0LEOwF6gj8xoXkpcyqoxNr8mSQNPhJeWhIFg1mlyyIRUVa05SIFKld1qc0AMk1n8/tCvzHpCbFScXL6o3o5b1EJetyGxdRmppr+AoovtQ03y0G2Z1uUR23AtbxkiZWlCB+g4Vx5eIm8e8uLw2s/AG6Qa8cPSKnhgJ2b9i3UKpVS2mHezLGTKHNsEVGxjKoAdLDrEaj+S6y10J3RdcQ5r70YuMAQKoT/NqYjiE825I2IvS7A7W6BIMFMuf2sPhCpT2RPtirK/ppC+Z8yGrW9cZFfGeHVTIetLNP4sMCc4R0ZBGcyBEv8gXFxRyQ1WTraIWQMJeMDGSfPwy8kwcammlgVyL53QGU8Yx90hXIiaUX+zbAmHjzk6T+yL/PrD3ed61A/eeyOwcWq0oQk30pvtk4VBeNw9vopFDJPJdoQayQREDTc6u4mGbj8luQlNtwtxaFzKTKjwm8ycMUxzGPyj38UMeuZePcJ3EAeMIAULi/3p0LHKnmJQliIeCMYZ1INd06BRUaH/TxlIICQveurvXIehKny9mq4DDMKStEkXVkMkCg25hlPVkEvHUB24FVjp5emIc2XCpJfN0jUzM59ZK0EepptE/CC5efI6I50BMs/ssxB/5mwDprSwVSHnHlcFZNgCp/ecTyouhS3BxwxfKHFNSEBkTayxz2i7U+GLA9jQ4C6a4oqf8Vq2KM7i7ShwtuBSZvgPWVWl6Vu5epxEatszjkdibEt4HtpcGaZuTdCYR6hUAWYn8IA2BqWS7gojfaJRV/jp0NDYXa9XvEvE9zLPHR3HBQLxEckUYyaoJk4uYeaUbs/ieR594MeMI7BJjaOieu3fXZeZozi+3lREEcDMCIvy7mBUaCBHZNcQFuqhRkRSA9X4CTy/QH0E9KfXkrAchjsQHB8X21Bql2GiWDZ+cthlVnRGmLLQG9/WwwYCrE3/q2T1aBnA6a0GUmH5OcDXdM4hI1NwkjW0hbY3NcleOh9Ad3LQ30Jw+pkEo+cPggLLwjKqUUGwpVM9N7OV8UqobRPP4yR2pi+57NKPBV+0iddQfClw1Uv6LubKBX02uRm/bGCtLqbrGTIlHDJ1aVIa9QoAon0fKfhh7nGAXxO6zIevC0c2UhttpiQEXO22i7orrmEaXYXTZSPKA8Z2BFlUlaHb2WDe/ox4IjQpQ07H+kaYT/Y72j0nrIJ1MYoBQfPToHd1hQG6wMCLZb0l+HwH/FxvlAYwpSj3u0V9D5wmLfVyadoLP4j7b+Xgd/VbR9DXrwsUskOgZEWOOs8CdayrparTSkQFVgYDDaMqgQkNqj1e/0kiXp+RrcFsrNz2r+icUeZ4DHbfrvTB8ENeQwjfpY7e8GmglGtllRAuSPxzuEwx6j6+Gn9hFehSfUNmO0ZPU7on1N1SRE0AqUZSjijRkVxXUkRAxmhg7GIjeP7faySGAGzViWCethRbzThYKDx5kXKjtbebq2wMbaAIamY/5XQsJmXndySfoATjUzR+Hz1ehCFvqWLuPQzr0pyjTindDIWDHwEiG5jn0zJ0NLcYZ9Bc2px+K1+VG+8vpFJPQkFnyLHCpMwNr4ZiOorfYQx6gbLy/AOSCcVEQJkWWLIh4TEWeuLDxYSaZydnwEfLjpjXgbrPi96ZlZcbyULIhTAqg6SFiXQr7W63dnJKzxrwA6QoV3TYQtGwRL2/03yk81+I8waZBFXNLjMABDY/KMc0VznMOU2HWIhHUpkUWcmitO+z2tdSp0K5CBtqFUeQzxHUllSuEj3J4ZLRresIbhf4gXB1eyIKPb0TbPbthVUDG3UVQQrChJ/Jz+E9mGRDQLtN3/I5IA7gqsqII1dzer78KAktdAKEt9S800p6puqQazddq0/Q8Y6ycVjJAEYshQunAjM0ev/TG4j4jjrZjjegNKfUvwKcHzYyNSLNSbqcNRzirL8Q1cGDANFsj4wl+lmggCD7p9G++uwtsWZ+iSkjzr8Q++A4zFwNB+WzxLlfSuQ12eXV0tEXTm1MW4rZ3IQO13DxP1s5m/TgjC7g5NlsdUrprBqvxSeK9ThHjRg+TKp2bYAiqaxd31bvbzu/r00QW9U9Tner9nXn7trZDnbfGRWRypXPCGTTH4u39vgO79x7mAglMMRV5txmFcgcgGAtPHF3Fri7C9vZfzNFAUBc8AWrAoVKrXlJ1bSelEu+j91ixD5OqZgP6upPQLTgASGArpU4AK40TUDQnSl4uy4nbTLN8RQKa5RJYvIvGXOiFao/1QYczOZWAS21BiEBcvmqfN7/MsjcUwjwMTgt6XfSGbGGukP+tusVeHuKA2XvbBULQfYZpIiL8fxrOKvYBTfhtRQB8IJdrYHj/+eMn0JbewYMQ+xrk2eOYqxTMtImotpuA8afG7Xu/0XkyCVBU1ZjFkpARSRYVtl1Se8RWMRzb7nyU0nmOV5GS0VdupQ8Qz02RijQWDtp/rAwlHpFBT6PfnnGM14N9oFpTc7wLrLXa4W5qlfpGeuinvmP54D7wqSr9G0ITNqFoilyPnQvDGCv2wnD8JN1koyOAT2O6SFrkdyTrD6NBVG82kOdPCBpDk5IYx0J5Xq0L8dIw596TwK1khVAcdBG4z0bISDg/ClFBhVrwHid3QX4kKJZA13V5B5tishx+6syMnkmowtM6w9jQRSxjaGFJu5fDC2SHxMHzsAI0bArNl01J8c3jZgCSfn/PoZy8fmBtMuYwsvRxRUFNmVKGfGnvcdApqVE9Fo8d9iHK/tM3UOSlmKQpeb5j7Komnx2O/By2RUzKvSCoJ2eKoRIDlcSQPJvRM9I8Hz74st96mmGwmhIqqkrrZ4Qfe+svK5mFcsQhRQg8vX3vNXifRa8Af/uuGksDfv+QtrLjGnZscmMp9H3lJmO++jyUH6Vy256tDjrSUjK2RBatgt1FJaPdYpQL/OfgzpMD4D980WvC4R3HIDpdtd2mRP2ZttmjBhAKwsbnAnX/+Axvg3eVnUdB8SDPmKLPibT2t44SW2WNZkIwhTH6CD0Wj2xzntFsBp9GJW7gAWWAymGClQQsKkbWFzFiTly6h4QW0Zf4se4yQQJymdfyAR0bzgkH9SM2OttGiNveotV+KcvanThl36/sNBm0MFSp0MDQvO/Gc5YMaZwvW6hwe32v8+c0WAMXrj/wnN2g8CrPPJ6P/GxN8rCIIov5TuTMD4yLeUb8t7BhEBPmL0YNWG41VQMGgsJTwzN50QNz4zkavfikQ37ngPt1OUH+TRVdXKrvwMQvD7O8u91uvCnU8YQzKQkQwqi+JFYJEFow5oVZlrPcJ76d16M5StLuKjCkZ8/EvN5+FcKgLuRK1m6klX1qKxLG1EjL3ORFbGzsECoHqwfzVsV+lWwQTVrvWmEZ5/CPiR3wPvPT4iNIiHwAoPCBip+AWBOmBYbyeMiMvC4GcJwgLAcU7cqag/JlnvnO45qXKtgptzYfilGUBLIdLzaYlsmwbXiC/8P7wof5rwwJnFaqnbcP/HpV7Q9sHMR8dGzw8r+n0hzK3v3wCvF5NO9eP5T8sdAz9ABO7TR1wTxBVvMqr6a64ylrs6KMwENKkMLQhpjJsh8F+qqX14ORJdUPE9R6w7NZKpp4VJnY3j+S/aI0wHl7c1dT20mrrKMl/QoqirKSponRv/aBnLPmm5qb8pRdC62wkDLsyz1EqJzML/Dloaqa1balI5Fkr1utkcqIyewK0xB6OSKlNvfq32bp9HjQSn362o0V1GZL8B8RMxy41bDpbNcARWGc6SrX/ODgEuy27wGnmTlLroJvqZaRm91CFvqY6/XcuJ/nCafDvE0dpNEoegEJXwJOj0e6DcqLyiH7nY21qElokBOTaQ9WvAonGKMry6mASll3//HegHO393Kpvhsh2oKXXOQ0omF3lm67pzB/P3qI6xsNQp4a0MBTyfda6yhHjxTzWYawqIJGK4xUCYAIS7sXrDChUmu7DDFWadWg1s9n8Y4fVzwGCpbXSpJ0j+BRrEVg3t+ZF/IZeJK31zBRUIOfaSTV+GjPBQyCRGsppF0KO9u0456X37azvIuZZvhV3l4dTvAFXyzjTkodJ1OyNCbCtqMx/zBDbOXgihXPqwGp8i+NI8vf3w1L6Klkj1Vg2UfwLO3tI2rbfHJLzv0/VZOcmkEsWKwQm262yFcCaLwtNFdZ/HJEiiHO+Sgy0KIh0PY322Ne3T0uuH653Z2RsvQSO9L8c9GMxkqpRc/FDKHoWLGiXCMOfteTuZjdKtB27Hu699ZjRfbBj5J/2VvkncJGSw7Upwa3pMxxN6UtW+LuR/DR/2079faUazpx0JFy0jsoZj54kKPiJHIuqXIK9u0wkqL1JvCaWkbINtZCOtreFxxf5t6yKEn7BftqTNpYXud64lf62b8nOwLjAOknqtgY8HQsmZQa0lZh1FVL0vHJgJxCmB48vbgxXZreEA1hrf2VNPAPTrQ82v+UDXTEKTC6uR4f7mQulhGknKH2r2q/h7jOUQ2QJgnqOfPmu69q9lsMpjcYQPQGVPcQAqThZt6K7tYKucLqQQKBMNwBgOHDDwPqmCKlOodEP0WYHZsjm1bdFEAWaMlRbQnI5iAZynXHmaGWX1I6E+f4hzkSIEQlLELp23qKl7cDA7Z507MSteCir9Q6IH8B/u4zG9MvRtAZS3qfiEhWKpLAl3fllEvsmDfDkHfkiL9fRQCCfMmKEzJCG82kDbr1BJ8BMJgga3yxMGNwz9u2HSyyV3elyUasblSMdQofiGWZJO8tI3RvfYjBMMTvzoiUBR1KgjjYD+arhjIw46NyYg9S6A2X+Bmg51AiHjhZFl21Fcz7LZLMp+1YToZoWT8CcfdmN15uIqg9zU3g2NWrnXViM92smDtsCiNKle8teB8pKXLn92tTkDza6I/+C27/GL9EtWnDykXt8XtHhGQO6y5fVWDo51hXM9UIuS+wx7NS6plD3YRvrRRq73wyBdtvdNWZzHPO9q1yW6HEsc+wBOLldwFJq9r0vOAoctztp8UiXB/cm30VEQk2iIjsngXJLbbDtKLM5dwwa6kWnY2NmdB0pBUlt8MyrkFiSUGnIelRFGxUTJkZ33AD+cmQyBxQxKg28RtFpssucCPe8xwB/iS6TtwFJZPl8gb0zpDJxbqkUETN5HaP0zRgJ9EkR70fhO61WcSKpqInCOFMRvq2g6Pmvimz9DoRvP9cMJT9gnKIAAmdOPiQLnMb03kc1ibjwHTcE8DkuuIWOUM8r2S2WLHfS9wbOMEs0Sr3/0X9jhEOV3yTK0wOMjUQdp5WF9hoLNqIaIfORA4zeOegFcv4BM007QZ1mmK89N63954c4JqfII5MXrgA+nd/uVx8/ySY4f891PlspSy7yr4A/uyfrD3zzzCjD8OHJEuICUGEu5QBuJQtQ+l+/LFGJieyIR6ED+DZkT23fT8aI/unkntg+u5dP8WV9rEl4dOtuVTZR12ub33RsBm/219PO5NC4kfamFT7w5f8+ibGiqgi7p0alZD453sWM2ErkFB5Ze4OS24RghTQ3NsEeA3Vse6V2sipfdEhH1+PrnE7lM6AkRsutBH60EQ+EUdii8+GmpCLFmg8eJYxH0ugDz8oNcgMqsrwhWEYMx0iUtuCWdJfclK5Sh9RXWIic6rSY2kwFzFWOxvltONdz7Pd7wh5vNx93sO1dj/SISsqk0+yYDvIf++/N9PxLZYeghS1+Yyxh+8ePG1Xw6XBnV2T3uqMGfVHJQzzTPZYGZy0H412A2RtvBLV7K8LHNB8XA7Pz4l1Dgk2dCBFlNgWdkx1wxnpaJ7oKgqdqDx6tBCEwmN35mH8lP3GoQWbibNToFLhX94GR0F+u3mpPs8yZejLckfbttrpka4a20xejYHkDvH2tsTrTPixhO4lYokvVf3C6njbq13nC3VKMa3eDdLZr3JK5VGkNkvvjdzBokrLXsoATQDfYNNvbEuKsdkrn8Z67/fyRqy++5TLhd0R0k7Kcs9UCgVCeSJ7HANBgAbe+iXZ/iJPFsXjzR0BkJOlh044qC9FLtmdJmGBoFgM4vvdQkw7fCSZzYwGCV6JjfsSuMvYjsOEzZ6D8aiHs8dNoY4UCt3f0lBrnFyu2fKAeVsN0j089D84xseWvrehusj9D6EpbEXOGHBY4fkaEtyp3GwEByV09AsKjTwwqwCPE2X37t6aTIq8xIaWG47Uq2K7ClIQISfMiQGx8GzAVINzcZTWkGYEfMlVPqUNBXlxCWnZlOMl7JwVZ25woJc+AKXQnbVhY8+HopdZKSP2DUDctVwlYLbSIDpQU6RZ2VeTO0vigkJWcHJt7eaD++3CHi3tyDfdMGBMxSa6nlNe/obJbT/wl/HzASsHVVxRVRNhh5NwkKJ1EefnhA2en6UPZJn1i6SYEOz647j8P/VhJbk+Z8ftQi0/1hGXnhXxZc8yfnVCysawL3l/Hm7sycFGalT19cxBvWHhuIDW+rjjyzxWWPn6P2HCvCDTyZuAUyTMaAYDOE6WbKqSpEPSg8ppgj2OtLM0N/hF4VtfMB6bCbU6R6o+2IVVItRaR6drZ8PbSiQmby1bvgpRdbiv9r8aWu3D2n41nKpj+5gaFklnAN+NOIgFIRDWTrjLEWK1+Ys9GDs9kKdPXEUJ3ME0+CAsU9YjYTizE95cNVQDncPsJh/xhg2mSLcyM1fDBlQhp8kmF0qiudVFAg8HHFEFOEDuC4i+AHTBG1297LEXZQmX2Gur2kjy7m1Rmgp0gKszs8//lTiB/A8SvtFFdphiOWEaTRHndhbGtu6GqtoTMp7vUN6KrdiUDVrNm19PGSiTu+s/E1ankxpU54zclwx5Fuo2OLxH9JDRz3qcUEWz8hdR+Rz5WHz4y+whS6FmHBLScUgFum0KJ4lpDBfsx/DGfoTqMQQDM/53OCu9fQHqUUi7zw6nTGWBJv4lR6/pVSHltS5vNEH82m78y1A/Zdj3W2HG1Afm3Jwuy3b8wXxMWTRhh6YEZSH0njXmjIjK34UOr72Zj7BEdBpqeV0IU2fIR7TOVYR0KeIGivUQFB9d/KYBcnInzp0ZYg6UwR+lY6Ji8Fxs0eE7yVWChtebpEv3CiTKn6PyN86kxI1lNYP0F97fTu7B9g4JmDDrKXJXdAGMEFa9IeTxznSiQO2KzYmppAGlS65hVMZnqb7Q8Lrio+sGmjgEiczvg5+uWtuC8jupxZ9CVwr7USm4tLx7DoKbt+563JNkqOcb/rzeKAniqjvfFY9doB4IGqI1Id3m8ycqbTXdHXDp1DazxqFMp4LLZys23JcrEng8nDn4Hhg0TShRTzN36OIhPdXchBeQD7asHBkNySehuXdT9WWjS3XozpSGnbm70FHpReynV3y2m+E8Tx3CgP3dELP1IpIjgpU/N4J65ZozKxoJBeM0hRMy5nchuzbLw6BzEtHUlaEOPY+rNweINc+V5g6+LFTErZWXXn3rR35pr7ar1cYL86xp+IDzuWEKyA9y57tghSU44LBewpclIE2nROU6l3OznIJKKC4/K9lVEvam8qsO6jjpUkFa/qjThzyHxDR7Wz9a7y/MHLMf33EMx4n/b/Ny42v0GRFUvG0rIUQLZ+nWwMh+zqBmoIBA3tSRJEMR7mJ/k/oLOo20CvJwRaMzghAjk7nyGlZOC5Hy8Dy04jWcA/C73bFQBVwR1lPgvYYXMpd/SWA/YxJzgjgcWT2nqZJg8r1x+TMB2JjuobvImVjUCzaMmGMyRzMKRvq856XzydEtAexZzOYse6r4weVfHuG87dvqidoF3QFt/P6KpaxQ/EkZnA8FDwjg8YrMy4axlN3TTUI5JIrDqij6R4cpIGzPSik/G9QvtUleC6l0r8xexhTO4QKCX1NMnnQVuOlOlf8nK2aDXTci8c56Q+NgNJO2cYK52WU1xW401iNmXOZX9ZNPtkrUIwXXyOD/Ms8TF4Oy5IyHI1e3jZ430RQWHcZAkroRzAGngeVtTfdhaiPyyvatNGrnGcvt51ctSqLxtzR1P3YSM5hXDeZCwvLfsbYBz0ooOx2EexVkn9P2YOXMQV5ie82mqEPjj39++EYhfnulH8yOQ9+WT8HzvhPG66Lk2znAZkPAKWG7i49kIyIAO0D0vCR0BQvegXwWw7gDt2yKnb9YMd8/HU78rKPkX37U7jWmL/kP1NoYx761GO1iSBYgN6P2yWOh8rIZQyYCgyuHZG7X1mpVuNEVfJLvhyOcO3/nmOP2ckKR7AbobA4FGdmcFkXHJOLFRTvagIESdYdtybJxPqdbshpqCicF2Fz/6jWTNP/G/pXoc4iNkzIOb82iznM/ZrF30Rhzed6qiYZozeIsiRS9/VxYF7iobX9L/N5Xa7p3YeWfTN9djUcGFCLNpqFGoctc8txuM+mGRDedy57kDiQRb29huICuPuhzhexiqq5vfZoslNRJzRF+ZrHSA1NACYLiWj9oYlDJmdyOU36mq9cUchDjWcgstnO32RwdYK8FCncqvARnpLinqIGGyKOxrFXM4VjkdK0O9Y3E3drRvjuoQjFjtXmEmQVu21t/BGdBaSDhaisYxswug3Ncg/O4d5guw19FPvKPkoHQyJlLtBJQhMblWcCVAvJjywo7Nxdv3CFRT52qJ8yOjJgppMatIwbgd3igAuBcZWrfRDUqyG9rRTaiAi89Z700bfRLT2pydmU+Rukr3qpyfmGqufRPtWGzTIv3qHn/j0lgQgUTX7K1TpL/bdCf5kQY6Eu3a28uUS8XRMyD8NmILwCqn5dJXjT7lIaftjTnR2FsKYJdln1lDL8KkZZNAJPArrw9tL1qoABK/dYw2O/GTe70Sc7lZ4noZvd2mfRhQMBwUiuF/HffDSFIxtHOe9/IIhhCfQL/sdF8bHphdf+mjhVdJkFOezSDlt40GHwevXkEQv3FK6txMgsDLV17/lPh9yccrLA9FTnDYnjEFwZJnc7JrVhekdU6CqLUyXBbiCh7gomJZn1hcTdFmy3J+JNmqyDWxcwyMadvVUaCcW7BJU4VtGDsWi0QDb/dWc5hNqFe93de6jhFwAHFz1ewtIkDcZNcjEEu2xBpvnys2lOPuRp22xqKaFOEcfCkIurw62X+/jiYJ6B/e3YjNVq2KMatU2VE42bI9dnWTFS/oDL6pkZqSN/NcH5esVMkMjzsE98FbPk5HH3R5eZbdh2xzxo4NF9qQMy5bsfj3F912kBVD2FwR3S9RPpixM8VHygJEYTuWoY7IhMkAD1vanMH0HGqhtkI/IneDQdXEzh7UE3pG71/Vo9pf1e4cgva/HdnevwnkN9iZJHwcaHCwsIm7eUTZC5gXR76fky762SWJrn/JsOmduLE2rSXQE7mnFEHzAg9LsPqYXlqGBQDMSyph1s6lhcbJtdKbucsHskif/4L4edaUkA+apoIjR0kEUuGJOOe+QDyIy2f71quIIKy9By4GktHIagcMn6GHX574/g2fqxMw76O7q88ZfDZbSN8/B60UjRb3cCari2fl+0/mFltVuB8hvWDcBoCsl55TgjfXgnG830h45pY2sVwcVPbfGq/8Y4ZPdHsUyHFqi1N5o7qNdcl5eUl5wXPFsqiYlfZbAAnL0FIUx4+E+uDBIofyGtdgWymk24r9zb7BnwUY3GuuA3jcrcNFPQtvSSmts75x9yDZecOFrTiWaHQrDw2fnQ8SrgLpD8+lnBEKmX82+osYAtaeXzJMrnwjd1LqUiNL+icPXKpjyWHN7zPYvW1YtobdR9REaEKckDTY9qurGiVE3b5CRQ9OrDrPLf/rlfCAgXpH1MxdSxmCsikJtv2jhSCrnTJbIKrt+GGDnYBqqthQf7X/trzamBBcDAJ5IAfwrlasoSPyKx1v6aO8uA0X/bitknXRPUTNP4xtQ34jFUJ7BDS4/i5hDLfygz9eAURyfqTMDKZSZr32g1PBfHWuv4sx1tGKuNqRpqMStNFbfSFZ2v/Y5HyVmyNTBlCh9STNGbDohxw/YF/6Hn5IGR4aPFIBoIHuuJI+WNVAhT75dWi6ojQYv7cGRB/ssPxkfjJ8JrTD3OGD08+ctXCU6524NFPVNUG7BWKUUnlOsP4mLawpIrfnF1ai3yp6Sybin4ynMF4jxd0+n+wUHCaA0p+rJhdjv0yfRCV5ry3fqBg2rkD2wGyh4Ge911UxarLTKnD5EagUYGNZ1X61vJAzBU0JbbxdEwtg1lr4qM+4L7KiWm76FzoEclrk7ZnTVEujJLXQDTLqnUIKNDm4Y4LOC9IEYXBBJ2q60ewCIdtDUHxJMAj053Ldrcf0DugFyk1C9ysTD9+JF1aLGmrU2hydlEzW/AT5nHs2zUU43+OcvVUtA2e0wACR0RpuL6z68xSCEWfjYJdF+YIw8R4Z+KkWat+HXj8yynza0wmJBIccFqhI9/uWzOzX+OQczMk8WU+UyutdE8/ISXYC1x2ZYcE0OXGy7eKMl5CYWFV5puI0P6uELgG6mbyzF2u8P6LCuEnsPCjKaHBtJQrnr1zYEQkZig8Ot6aiO75dea+tqCOj/VhVosW1V5irTznvrpZUEwD8/dXEd9m9xOursgOQuNKgHsaCi7uwlSXoPbJ97nyZBQPQuc8AZhFhjuv/CURh5XXe7IMyn+wgxxIOgUiQzeYJ+xISX0fbd+e3KJ659BC8V+gdy11b1F8RzfE38g2T/Sn7gRmu1oUAzAfO228aWEwIT3OrB7KMHjJP6xhs6AslTq6NUwc11mAE3DOFbgnry1xmg94qpDWnNnjQab7nECFiRnRpykpbAgLzC20dsHrtt2/Hgb0qOqRVT/y1sSzI5WchaXGGD1o5MKI6aEFdcG4/tq9AW0Y29ffb0WlylviPY30063R1x8bLF6A/Ac9Ve0mXTmIWAs4ZRNd1mRlBCQe9MgncTI8Ly7uvx3BJeVZlyzFHpXQw4PX2SzzJkX7lSJ2s9osvTDAF+jxFgEPRD0wmYWSZVlwl7MM5X2xkLF0X/xelH5y+3VrWKs7H23XsGBjwjd7PABO5CZTa7MuzVKOZA+wz+fZOzi0y0KufK5cr8vVcbKBULtZwn3kvDUFM8GycQp8/R7nAyYQ+S1BUyr5lIKC5ySCuMr/fay+VKm9OdG3Sz/jrmIf8f765YZTEhrd/G5Q3JnmSXV0t0RgLmb5iF1aw/Kvqb784i77+P15jn/2pj98wW4C3C3jLh3ZF6Pph9k8YAkSfBxOjPAL+lklvNH0DTuxPH3vc5VxTSvcLU8IAYNqDyRZE29np8kT2pm+yg5/aeo2t0g/eDBz2mJtRW8a7P4U2Pb5+eIDeeCuhh0MM1J0r6yXiGW+usUcKpEqWPiWhG1Wv5YIPeD4heoCj4W9OPb0IOupZJt0yzPrS2U/xtBzk/nmrT9iMila2yVWFro6+/4ZVjQFEnxjRwDZNcupJ9B6I87FPmq/v8MqqbNvH7SHyNPLQk6ijLuqvURqx9GcwD8yKYftXIXqN8jjM5H+ymsx1FzdUPpVdVx6NJSbLPoZcd3pfDelpsxqPtrrH6CCtI1td1aaqdoomCq9l1TSFHexIID+56oiyC+5tuR4DGPacydaUn5pWeM2KZcXhnkigQdnJbo8ayi4rw7cSOwQ5NAvOR4pH8Wdm9C5u8R8i1Dcg9ih1pvLhq70ooPqj2RycOlYBUdPNWyHmr62GkEqxeIvWS0MUCc2yp+TSq37Py2O+7vAbSHVJ964Hls0ZgpjKYVz3VIzqICOmwraPSetm5u5HG4Hudg0NWO93j7Sff8I9oU9Gh7FASYmNCCrN09oNy3YaQ4TFSA3JEeW5/XDvA3i54HdmtI4GxXA21QQsgWwPKvp3GP+EDO0f1yASUtix+TYGjLOD+Jj8kuVD1+qRaNqvSON/8qSRZZd4e4eiDwd4RcAIgrmqjqcnjJl6ikR2IuxhBHb36enbIoBB3ismPFf10tyPScZKd+xkqmT3IFR3Q8Nleft1LSS+61pTPVKN2MgkCe1cHvLjlGDgORUVsv/Dw0FYZ0QFN9CTs9HFn9bZ8YkBIPqf6DdkGTVvgZzelLQQpeETq7NhF2tlzn1tDmTLPDOBk/7k3xJSUARXBfmyxTk4P7HxT/dcy1Y2lT0mcwp2w2PHz7NoKuRWIYRKHqwXLtmNNxvx9b25CGqUQ9Ae+wQJx6xoQYFgIjfGkpHBsrEmYu/Xvekq0J62ojhIqp3waG7Q3Hq2+sX5eZ0goc8/6gZcbvjVgDXqzvE+THIAM4HiEHoXEtENl82/Bk/f2Dssnfmcv+mdZ4iXcUoqNk7ogp7EclJvFyEcqtG5Dsc5tcg8dAwdp2IT64LQw8zsSDf8aYnKGMl6AikFup9c4W8G4kJ+4UjU3fOpBuc4JlOc90VHTRJVhsHjFCg1XOSNNs+DWzdKfQtV/AelyK9kGXx93WodW2BHb2jX8rvOO7yQDVvcq11Z1PXkkLw4RiIB4WHZzGxRTBWxOE6SqYOYW14wcp5jjfXz32Cg1Fk1V6DVbsjAYAtHmlUtswMG2dco8x3/2ILGth9zrYI4I5YRtG/qIhG7n2LaKe1avbybdaY/23sHpTqTLYnH8LtS/91jlBgP4Z4u1LUd+VRnNh/3v1RuEJvr/SeySDm5I35f76qw64Svb/u6Zv9SMUz3IWvlu0H0P8SL3XC5KGq26OlFBdzuG1XUvdS/8G9d7rWp05f7ho7icYGpx9iVZIIWm9ryBr0oPUfJ46fK6JBGzPiYR6FJa9UQ3Rrv1Hjw9pHqAeyYaiv02dBHI3zwBszfWiM0RzD4i5fN05HkHHTY8hRQEfF80jQISLmzgMKA/EgQY4kmRZffUWUlsP0ZXD15ZNEQjtABkxBV5VzZpPYu7kK4D7meAXyhsRfJL+OAJ6aaDQiWHlYdyzuUePd7bIpL8wfXhwFfgXilSMCPC7bnHAfOVMEWAhivTbN32UFidkZ+2gwQduLGx0YcRO5JNXxE518POe4n4L5EtuwVnOE/LQpsA0Zw42ivNSjAt3/D3QIMRXc4UK7TDdseg8oX7igGcLkW12JMDudYTWCt4UDUtUJWqO2YnTxmZNpO96wzIIYx9cvDrjKkqU9TMb24fOeHWsk/kYamQZwPIRRtdFczgjdhUj3U/r2QAjUlB1DinTKTjqncgerYQbA6qTMNgD/vTe9plCSorm4l7Tj5s2/PqTXbks/0rlH38KweeMaadnlIxdOz6w4JYAbNmEU6vleYgzGFOI3wUJA9ebWQAYksNh3l4V2LY13qItW1jAQGK4yoRZOV/tus/bSWntJ+8+GNq+lxcYLpJlwsr60g8+3eqOiUU5K+jcgS/FZtcOA/Sx9mO+cMQjJqaBrf5HTNeewk5q7yJLU3fH5s/bnaCtBaeZoRld8QTaEw6FBYllelH0jfXPObwXmVIq2+d8f6gViZ2JIVctHOSanwjxo9a5OPCoqrP40T9gERIBqziTO4hZuBJI4UZ4uxn7Q6OGZTm5NmUqmc3SlJWW3NVZXa7rDl/x++lQ+oXvCxLN6Uj3hvmUxlBxG/6NMFChiSihS4jkAZ13BIxkCwmcgTKQNYu7qt7JeZPXUtjsYpkSAC9EWzlVI/kNSkNJVmLrBwjs6Rscrjt4MNUlUoWGpRj0RKzDdQRMjrKWQ6D7CNKjZVMEv2JbnySsMWYP72q0nSY0YUNKU/q2FS3vCYN00wKsnSx7y74fXnCoZe6vzLyyG2/QRjAv/1u1n/ppHU/AIhJz4/ZvRBtR03yHH4digOMcr25O32L5mGaAyfb6VDdr8eSRw2hJLMqIPG8l+d/Si8J9eHDGAXftfbWoWaJxJ9wDEDQgeAlRvewK5yzi/1lXu4tX/02oMd8CTgFjnoJnB0Yq87HE6NLLWk/cjAs33PQPMudfJ7A2sfcgeVVK/4dHcd3hGP2ROUdiF5LfaBqDxDbJj1ledaowW7jCJRyd1R5xBMPHKEmdM9he60nb5p+dstNqOgCM6UXsxZM6yYw97ZJrSLJRwpdmOKoJ3IrksmVzKTxutdUackfRdcxFvl8U1E5KSSGpszHGFv504ZwN6yNA9IfkF3N3zrtET/Gc1ahdsBW1rGzwaE5pPzXUrXu6w4nvnCADScYDMGieujkpAmkTVhYZP4nGgLJrZ8e8iN30455LpgTYVLLffIgJHqhfyooYa1oC6fz3aB7/tgkcH7E3iyR0OenKw8yvHWugAP2evwbb9bpoa3N1XvPopcUyzhdghUvZRLtEdTpnPL6zYChl61oHDSVqJOpre6gcCdKMqEBcE/qI9N0nrYc91+kL2Y0VVfvZc/lqNOsh41SgPsJ8L0pfYG6nunShs/1b8OBnYdVwX6yE/4W+QGIuT+2mTuqnHGpsvV7V+uw9SDbSV/3W1BpVpjk3NzVSa5ggwi+Bl2ApVrvLl0iGM1ECVjkIX/RjDkFKQwOdzoFK3OUACLMTfzGBwNQB2ozoBTD+dK/SEGg1a3d1zgl/BdK50iFrBKvbKd9hmeVkbcKGPOfyG0hP9t9KhB8904w7kFLtqT3XiVwJwz10le6dTcnlxX6My+gkl1Vv/3olKORfxBb0oB5C0gOCvPohPMEvMSNknvTNKBRdmKCpgQ8oM0iOkFeOxKtfdlZVXPJW5JU5D4UaDmPSMzmqYFhZ/SJFrg5GpIbNRHFI/YozaT54qzFSzwD/A6+2bIPY2v3HAasz/RUqJ4CWouhFtqY9RBMkosQX7NFVG+rYzqdWhxh6/g1PgN2njn0UbmHDmBQiqidakQT/fKKIkuYhqw9f48lrKfbqqixmBOu5uFK8ZJE8tqGqmIsc4UI22h1p+pAisP0Bep5dka9HE9bX0X7A1WVer5dBb+mFz1bLXH1rR+2W7t0/r0KeBQ1XRZEO/c7jvsRP63NJBbRg8IKQI5AxdUVlslnMbzCfIGaooWRaNyh16Wsc4QRPWzXfiKbl4Y+lkWLsz1TbI+flLy6lB3iFZA4NMTTzkhY1DamInbcqz08OkyEeLASwc3UyafTudnBGmnBXutqylDYAapDO0myioDkjjHYeQ5sJx37HTfy2RT7ddwSer8vmMzk59s2o4Rtb68bAz93iuzh66SBF/9kCs1rjP9gxHxGW5cfXSaZCTMCERdHtiOTKb2EC0fPROEMjp0qvU0yPb2MOSzGW6FF4wAvZk74vrvukBb+v+Megidk0LSIndzocANPf+ADrHPB0Z4K6/9rCMc3tyNBEKuHI7Y5CYbH91WOY8/tJWTYdm4p3I8ISliDKOSvUN6YmPR0DEegwcggD1OCMSYdMwHzeY0cjglwYKyZdBlb7XDILilyB1HyDQ5JiZRs2Dh5FKBMzRQlQVBM/MFwgkbaS/82hgEfJyhEGFRmGgY0+9FdJYlC4ITM7GUAdkYxRR62Sw1aGYUDDWPvTFgUERbMygDUXcG3hAH+ro+hwJKE0Mre7cZ0FWqEztWjrtQFdioiLxdQDdBSzf+6utsFCsbxLKll9LR60z7cxrkA2CRPVzzmLZU3APCLO0mmucUXd1mwc2EDE1JvAHs6n7x5HTK0gbKyJs4jCRBj9wP7weeiii2G3zfR1XWX/RF2juekt3In6SOkUoX3sZCnMQyjLteUfqsHSq0mT1HSA35EmGQYv7Dxu7BhgSFN6JGAtncjd1c63zmfps9ZCEeSUWVLrSSzlyjrSPtop5B0ZnB1tXJr+lJNbE8tWM6pvnjwkO06710goU2T2a9JDOxdVd/mmEiRf1S6tF9bRPwS5kKBCwP9M6jfy3xUpNmzIE6bFZ4qB6a1ZADYw5YNz9s9r0OndA+f5BZUlMmWjNqqmduJO7zgZ+Ssd3YmoIdnoMHlClqA4827xPlCWMBNGV0t/V/7dBvQX4u1Gnva0G8AzWRWkHv1ZtNTT23kd5+38F6ejfYkZLYK+ZDVKs1bgI+saYxD+FmkEyecwrho7cdzwUfv/K9K9H+KrQQ8251bs2fBj70HbNXMRzpfAgCVGTEMYgaz4vMFCFtrXuf/wW1KneIIi2X5/4Xwkm0L3q5bRrjbsmPcCjLvvaWEo9jBu9X56CFNM8INcWcFMs3zqr02cnqRdWivibb8eSzOzxNlJbUy6XrAG2TC92/n1Po7lYaAEKNovoym/hPtoXUUsFPJ9SsQs8L7T2dxIAt3cUnIp3UH5K6yJrpie9CtNNPWaG0j5A8ZUmJdLJll0wzWl/qcIl2dGeeY4ymLHt47QNwWRX1V6i2T9NERPerHX98NqC2EFhbuTAbOZXMlVd2IoYvtSDQivL+9e60TFqhyVDAFIC2d2rjrVY5dAQPpdBOfgejtu7AhgS9iqKvXZpgzKdrSgEaxI96msCIS1u1u2mgF3zuT9toAAh173x5sL3isJ1N/tTfg26yoDB7jx8FGLcVa2VTn6qAElbydgfAQtQtV+hZJOPgWBSAXUWRiUzHM4qGNXg6DmbTsKXdBbXOdo/6NCV0K2LXxarCCMH0WuzpIRElB0ctcU1mGGoF3N0Ef90kTqySc756iw1gcUk8W3nljADS6GrDRGzmtb1qAR5oJlxv3anJZ2pqfARIng7+UuxXKIqtPBfA/vUnAwUwZLoO3cLOSK2wrj+Zp0wAYnqwb2qAX3lyKKXsAZNheoF371IyDHbHz5VL8yPMvBwhODW5gUw0u/7VEPBu6QJlUSKsfXXuvO6IIcZ/xNXhtWRA1CM2RZE2edOXjOnp7ivDcBzRRh/QI0nknVlfBsLZEROH7IUkupYlp33t1EITNQT2Zpz36BQu1/CiCVC1olxi5ts3vliWYN+7OZFR/+xqCZ4xe10woZpBtHa6B9IXhjHT9pEWU84RYXfgSa6iHu6P16z8mwo7Ont2YxJ1CGmzgnu05lGHV+NMR9YU0uTEO8EUZu0B3GbOBVQay8hj+o2RTEOvhxEE9zqKYFrTLZIqdWwne3nEXYYuR4FGBWdDG04CgZ5vPzPhMXFQmOPDohkF1ny9KXBD61VpISWniI5ooO6MsRGBpJUmQbP9fl2pI5V0Mr1oEP6YavMdOLTXtWtj+EJOUkYrijTtW+Dgx+QPKp5Y7Z7lAbIrHe/SZyfe+VwN7X1VnvKCbf+mYskH9WtikPACr3W3kAMVZbEhxQYbCIcFi1G3ryQBdFbFZTD0PLqSosqR2zz9J+LvKJWnGq6nSPZ+yk795eJAz3WD2hdELDplgd3p9qcQAQ3oXWZR+GyS9FP3zpM/l83kcEDvXK3E0NgcfD5dbViTsFQxToUhr1psHQjGdFd2CeUqWFubwRzpOS3jg8MKnu1l2INGIzSvv4x9dodjQR1v9SikTiutBMm10a82eeDBU8ML0xwPxgePCwuYGpkGiHv92wcer2ER2l82EAdVhRLftalMXRpvoHD8wPYcwt3Swk8U+CgGAPJISodqcQUlvCMMeE5GVSFGs21rh7Q/k8AeqQuKGvDdOixfT9CmhWh/gv4f9dsT3jnX0DjKeRL6Mo10ndmzjgjA85kl5zY2Al8mn8TC2vQsJFlIPpMiooaDT3MLLCXMMKZIBkn8qX0mZw2MgEj0noM45c/4w4pkwpfzyLc2Iu8T2CsHD9nSK0+3Pk6ei4BoJEbh2c7TdjQqcCF3TOiE5sUDeEOmOK4ArQx2QOQ1mhVP+XXHQ4/4DgHz8QFMK0kpsba2o5Zob9AobYJhnwjGRXM+vpR0+aA1vvzzqUvdVoM+uiR5H8/nTV7Kh1voPmsRlT8SolOIQZ/p3zLO8PXnrmhwMHJP4TM5Ad2S8BlYq50/4fdijRBRby11w3k8r7LbM8TguSMYpuTsqs8448YVT5uFZ0EyFdBO/NX5R1FzEBS13uuyaKqkMHbuyu+umE5Yo5OWIemUQ7MjI+oZBEai1HGNHorAobDgJyDK0ByCR7M0DHaDKdFGYT3qf0P+6rtP0lv2NEefX9S63dOTNDvBJXzR9hvUGs0GpohCzec+W+YQ85gYs+VMf7sYcssyO1ss8WO27jFTK+etJmGa6UmGxTpjpka7YWTo0CvF3JkKqLutjchxMD+XuShjq9IsHAgaguyBRnVUQ1D2fMDJrP5kQKY5Jm/opFS0E1KbAaLv0dm0GwsxVrLblZM/YL8W/I/AIrIdJ/yEwr1rfO1WZNwaNG9X9/40bOfJG/euUA8jY9kuODxsvq3TgLoGyimRaqMpzbYoP/wCjU1FJ4wkJoGgLfhloYXJ1mtnclMrP1kxxMAVtj5NT190HLlt7IHWlQ1jiDr2Poj7XuzVgzt9BKs665MewJAmEpJbKsJcUPZ/XOPGCNc8LiaUnvZg9yAf9jLRUXZ7nmQjb+sSYY4RiygCKL2mMY7mBnJzlQG+PcW21GmkUrW4RQpm1c7I45cot/vXwnsHKL0isPfKoDb1dOEMi6XfU6GkQHi/Qsyz2179f22rH0gAS3t8ePJIEXkxXzCmJotdlpbZ9j5WnxkUXTq78cKL/JBsQkQ77CAbe+X9RiMIb9a2ieleA00Hgkj0Ep6NCiSO54hPafQeZMZIOhqgjhFKGGfsWURREudzXQJ2gx0H7o7tmPSaonXogTnu2sUTrH9/W4xIMvmP3o9BOoKGDJWFjXO5u6gyWmqqNf+SoEFGr9f1G7qYP/X0aSBL6PqoS3UirOeWvOxL+3qMAvAPmG9hUJ95KLNo9PQs4R9usOSSPyYGJ1+fS3WuBXXy7oD9wlf80GYXL/wiVuU+6VizxMvIiqRLC/K+GW+Tm5++KjhzhIs87EjsUUCYHvoBwmR/Ss0fVWq2fD4xu0mgWqqJHofiY4LiJcXWwK13yVgaXetCA+pUZ4JMP3HolrHSuaL4CtjoJoSaYeM8iAp3+CzXjBtR/V1GuJYHgLONEehXAo0L0Z7ipFv0cQbGB5ss8HbLlaampla0EOd0PVirgF5KFO19SOb3LQPj0LVJPVspkWGYvy0V3Hn6Uo1EF8qdH6yktcNhXLF7ehv0Td+Pjy6uv9A79XK/OoEI5lvDXWX3JWgfEBeXufeZc30Lh+Mz794HJD5Z8nlaMRWPSofNMzOTN1q7pBk/+0iHyi5dwO/t1dtVRlW/FpAkfOIiRmVRefrqraRaD8bqBeFNRmA3G9WwLwJir5pkIrcSnQcKbqqnx3gC+Z3rjJCW4brbBeyiqREhohWznSSNWWAcTH8nQEJB/6+XU0f9mHG/Wir4tDXDMOSf2hcWBzz8F286Jy+VFuaqSQ8jpGk61Tv3YpwjyhLIv07YIwol+LYnIt0cQHH0ySeU/ymi3tZGI3394S2+LioZtoAqf+ZvOfFogrfUA25e+Dmxz6PPEtIvpNpmP5Rbh6KGooommdujSBeve6Z+Bk+0l3t9TxXP0ZhcsE7ov7XzktJ1VZnc+HGQeDLp/XtuOV6VFL/0ZVmz+bnOm3MzcEXRtVy3ywg+TOsOdzJNwXIP34t2Rz8mwNQgvfH8L6l577TEbiaqYVMcr5jCvkgDiF/JUxXqFRFyfWlxVOKd8AR1PzZXLh9GSYbv85uxAjSSIuBp5H91dbBdE8c+BHchfZhJDSIxBX/Izx9P5vmxABTH85F6Trx9MlKUlT9JYczm0kZGY7l3eI99njj4ePOnkfuKumHU64rSe38nQ6awbKOUPIRyBo9H7eOgZ7h3lqbHEdMaO0OmDovXgw+4y00/NyNAM8j4nScWztlGP4XySzuV7d8BmiUg8eC5syRR+xA9O3hILausetAebfAgg8b1Z5r4Rl4Rmqv/BEje486MkXFoCMzj3Atkl4Bnvnl+fE0Y3ggk2HTd3a/J4VC9LWH1Kyi5NcH4suJeojPa5sCvmZNjNeWudaUxG+Lnnc2N45msq4lVfkk2lWpurctyNLyaCLUrYiG80HmxBZaU1Ukq68ideUgdzqmQsR9uRfIWPSr2+zCQIgZppr8l5u0QyCLte0CwyxKvfAt8jLBmeEr7brNE8YDOTX0hCaGeyogOWEIbstMTcAIsPEbmTGCOT/GOphHTIy6ZMn5gESI8L8Zhz3eh5GNaQlNoMv10QLVK1zMmE9ujYoriuNA8YvVsKDynHxvZqQ+lJPAJsIVhTTrpCqKURzjlAVRYhNlrkeWo5WOX6ER12yXq5EslGSRHWIeOGfb2ZC9seY3r7RqENN3dECpDAMHF5qrSGzRpr6S8fjei2gRkp00sDbt+euGC6Lrh17LHd/O2owW/ffL03Lgo2Ys67iQOUHKh4eM6qp/A602COeFGluqN6u178MR0pS62bwtMfXSyAuD/LDx8kfcDqnUwGWrYidTH87DxnwNCgtVmvNpGNmw8wgLFEZP69iXa6wyAnuixC82VM3olJVPHyZrLzZSP3yuDHOcCk2z1t+0AvOzROA7uSf1Xfug8KTxEB04yMu6qJIC4cirPZ5nDjU0W/s3FPaA7Wp/EvCEV5k8Vs/s5GV3namw42LkpCfEU28osCuonZKGspdvOoKzQpFuIRN4NcWIiLvemOTyHfIFabTSJga/G/JTMaeRP6z/SOR8J0MpCM64NAVm+K2bTk65fJLh5aaX4dguKe1NKf3Ih3CHdzSMUJRq85rbC6tUYPfDM1PB+dz6vzdLIk8cfhuiPg2xFlRtAkeHZVl0vXdgrv40knPioQXz7iS6lDgii3X4Zm2sPNe+Y2825UGyK4BKKHm3YmuCEEoM0qcf2cJGkLcAoQD/uTqb9nbJNvLvg79ITMvH/GJBj6hxblA80u/c3Nk77rRsMpsZRaJ2LCmVcnMaLd/L9IgHuMDYjqNDzyxsHtvykilY7cwFz072U8PEWenM9+Zd5UPAjamZ3L1cmO6TzifG27/eGNhRaut+P5o+K6mrSmpxp6L6KlgU25/XQ5GiEqj3dzm2Ew+jIvKolJHSuLxoyTIA5EFbdpRp/PIyaqwyZzcmSzKBrrRUPbXKTS0h4rnKMc+q6AlQ/UL/RScYuyNJ8REkQyoRaJUcgKTF9A5GK/XSGWiF33axg2eFKC67sKKG3plsxX7P3gEAkXch9Gz1C1QiD40E0EhP/FPzeVYKbkFRxryuxccvVqdkH2OCqJBL9GLm0rkoaKOUmV9szZmuEP8zW57+gg0d4CUw8vRzj2a5tNzsembRSPALsKHgk2VxoQe6LOygctY6/10FF7V5Twiabfh9p4wnm5QJv11h3V3VpEPcR/4pNhRD31x3pUgQtKipCKisNUxj0m5MhMUvDyQaqDjpvUapIqhKnCC6uuONSneQa4KfN18cqRJkMT4uhDCK5lkXMpsekGArcEKaPfnT9qGiXKD6bn4M+pAIkXbdcXxFzcpGbBdvpIrY8+HlYBXjA9+3zxzmvgFxylRkFaChK+nLHPNwkRVSmWPNI/G3cXeualq+/EW4vLjiuD+JUycKw9HsNfDMQKP0c+72Ccy+EXdDsJXXwPeBmMWR5uvOcTQ1JVoIGrr7J37eJgaqJeq6htVqJawgQSzKQl0K7KrbfFdvJcPZwctunCcfKxIlKM/CeLf92StPBQ3n+0yi84u6qE7R885Fuh4FwhDoS5Rx2I69SY6cY5HDueEuWRkca1Ag5JQTYoFme2Xex3yr4jWhxLU2FvlxqaVMEiJy5iyPHj5ykrkNW8ueYjg7AWAEXbCjwN+7KDPQBwJLPYrbMBBwE1YTN1TiBlOcfLSbP/8dZtSvdqn0SlhY/bej5OTnEuZHCXdJkoWIbZkH/pkbp1RoCjY+3p37D6hihrRlq/Gsd0WBaL1Ei8jmgbQ4WJH8jCDh36XOUcPkXQdVWWIepRKF1DGy+BbQN36SHQQ+ARevnOvr3TwzoTqr8GUguyXoOxMmnRM4ZlFNAUNVzp9FW0lU51W2IkoGKrA5NaMbsUtL+2PnaJ30AnoWQq6KS4nPT6N73LNqZIYYOY9SG6+ZWdb1ymR+6sdu+Du6bVv8JOJMKZPyamMDYNJZMvZ7+ELxuFBrnWxnyAEJUt4+QknwAxBPIAFXxU/iLh6l+UzAJ1INfVdnw6WXwJAF7gffbCr6pfK7OJVt+jF7CciCRapPuJYMusD8Igx7styFCW/3sBgs/xHNtInv6vjgXwAdfn769/RMJAGGq+uy1HjUOEw4u8ycPuPbzBpmWDSYy++FMsQ62cbBTsaRAPaYpwQbUbWDY2LSJa0gyVz24AvzRWnfvd+fNF53LNemL7SxYz98nUuotD00sQYYZ8GL9f5YXiBg6ciwGaUPTqUg7YiH9HuSuWePigZ13d7GTzInLZImRWJE/JSnMn5VOhgoNURMpNW9FEbFZEqs989lXA3FR2KIB5pCB97b/1mw2r3tMOcSnCr7miFQDN/snUbF1pG50Cl2aLrXQXdcfQKtGh+eBA1AjYv8UlnbKPXCI1B4L9oJIAlhKRXmmxOj0SlKvYZ1QmEV5VTqpeUmfhnxK//AO3GiT5rb7+HaJQ1+dZqS66lSJfHJSnsqq+rWjJoPZwhHIG67Dro2enPlqZkFpX8Hpl/IIOjUQD8t1CeMt5BVMDW/mcFXvollPz60s+WPpfOEu4BVB0Ck+3+rfcizxrbUt3k/aN1eHpjRfN0jrXQqYGPIZfDmfYuGOHL074YJ++Pr7HheAJqzUUZdaxsoHLPNIfrUcKL9mvG8xyM+nvQq/ZOI3DX/WcvVwBUgBAHRFfVyYJh3FnDWdtMvurfiZva8RA1zxFOtvMZiPp5yHmpspSaFiWpLBF3KbgLeoMnGynZUwdMadrgJdHmz2aH1lMgL6sNzPC0xsQnUzjnp0FUcujI/nAi9ZHPdn4pAsacvsC9yinQN8lK9gWEEo94V5fwF/rMMrD3yrtoV6MxDcgrsJa6RSVmKm5nOhlULrnrlyYzS5XERRhOHO0POn7KYtWGLAigs8R5qp6p8/K9SqieK7alBCkSxD0ibF9h1yY6yEj3VHIdfBSBiyPzJm94clRgAN6IdzoeYeqwe/J4kssslBO+wUx1jH7mUjVQl0sMG2WiQOQnIF+pRT2N35hBkMb2Tu/FDG4wFaH/Zas2MXhS2Qinn4WAb1U+t9zUMMN6ma+LrwWO4VCpqowgKdKluVvBSJoTf1/si09oqHxYpixuZcwJ/AEUM+Se5OqymcjzuyhONBa8UOQnwfcaFXiaITdlhBYSQvfZI//zwBfWD+VC7sGlCvfOb7BgfYgjextpthBwVrKxf696SCFAxNu8HwS5tmdG5V08LyUrLO5nBn0VsA5QjVz4MEE=\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign 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":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_update","assistantMessageEvent":{"type":"text_end","contentIndex":1,"content":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign the UI with the design system.","partial":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign the UI with the design system.","textSignature":"{\"v\":1,\"id\":\"9XoYFdQ5cuW5usbAoRC3/ZNYbI24ptf8dmu0cbRRyNRhzcasNoevA/i4gLP3SP/7541RD0qFn+tznFTjJo+Lcydr/pneI1z0aQpiycOtz2bU70N7+oQHOCkh/UYUwRRFKkCoL7J83pzhNACy83F6nHPl4rb9DmYuMk1bGOOCguhC0LS/vTmYQ7yNchCq2jBp8tvq3WJhVD1Aa4xKriH5Kh4sZ9XNz/ICzzCO4ET0GhNt7IswKPkGAaD+WwsQsJ3a6YhtjYXd14W3bhvy+1/x0LTAnjJ9S43g5pHqtdskU2NBRktZ+Q+BQgBO3Qzvk45HK+gHaNChIkWhJ9l45aeVPZMThj9jw+BZKmQeV8RE/wZY4G52WuMbsaQcvNho087kp078ldf/TmhS8LYvXoegWsMhbWxw/WPr\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}},"message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign the UI with the design system.","textSignature":"{\"v\":1,\"id\":\"9XoYFdQ5cuW5usbAoRC3/ZNYbI24ptf8dmu0cbRRyNRhzcasNoevA/i4gLP3SP/7541RD0qFn+tznFTjJo+Lcydr/pneI1z0aQpiycOtz2bU70N7+oQHOCkh/UYUwRRFKkCoL7J83pzhNACy83F6nHPl4rb9DmYuMk1bGOOCguhC0LS/vTmYQ7yNchCq2jBp8tvq3WJhVD1Aa4xKriH5Kh4sZ9XNz/ICzzCO4ET0GhNt7IswKPkGAaD+WwsQsJ3a6YhtjYXd14W3bhvy+1/x0LTAnjJ9S43g5pHqtdskU2NBRktZ+Q+BQgBO3Qzvk45HK+gHaNChIkWhJ9l45aeVPZMThj9jw+BZKmQeV8RE/wZY4G52WuMbsaQcvNho087kp078ldf/TmhS8LYvXoegWsMhbWxw/WPr\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"OMMDNnuGSiwPAgfQyGaBmvkjrSxub6IywyEHJahEV0XP8UDKQW6sSdDy1MpVnxfqGljdgis3vwPbx7C600FzTnM4u3B7NxRXpu5gBxNslMQx7numlleM0E/oWuwpBF0Pv/DoyFIyQwFe0r18VSklrsTiEzw0CfVEMCrI4XQhzZE2aFHqzwsZyYVwzzqXaM/NpALlUkbMCzHwgQhemtf0Mr7eAEu1+gfPzB0HDNsv4W8ro4DstC6EWecheUgLv0S3pYBjkw9FB+woyvnX2ppNtBE86fqfSghpZTObYIVteI3Yb9SaaRfq5oSbQxDHuZumQM1uX5DBfP620vfV+o+JCp+tIrs8qsBec/L0QdhcB7ZPwDBih89bIvoRohRViykNH/sZ+KwvppZo3IDn7JePkyx6+U2O89vV0Q=="}}
{"type":"message_end","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign the UI with the design system.","textSignature":"{\"v\":1,\"id\":\"9XoYFdQ5cuW5usbAoRC3/ZNYbI24ptf8dmu0cbRRyNRhzcasNoevA/i4gLP3SP/7541RD0qFn+tznFTjJo+Lcydr/pneI1z0aQpiycOtz2bU70N7+oQHOCkh/UYUwRRFKkCoL7J83pzhNACy83F6nHPl4rb9DmYuMk1bGOOCguhC0LS/vTmYQ7yNchCq2jBp8tvq3WJhVD1Aa4xKriH5Kh4sZ9XNz/ICzzCO4ET0GhNt7IswKPkGAaD+WwsQsJ3a6YhtjYXd14W3bhvy+1/x0LTAnjJ9S43g5pHqtdskU2NBRktZ+Q+BQgBO3Qzvk45HK+gHaNChIkWhJ9l45aeVPZMThj9jw+BZKmQeV8RE/wZY4G52WuMbsaQcvNho087kp078ldf/TmhS8LYvXoegWsMhbWxw/WPr\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":7101,"output":2806,"cacheRead":3712,"cacheWrite":0,"totalTokens":13619,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"RNStl7VdqppAA8Mx9QF1JPr9udtc15F0mYZem+riZ9KHF1VI9IjGmHPb/Y/SLTVvp3q8J0eQRuRYoA5xX343cHZ44rzO7igFycJrxJklgAUVzYsK5PQIbe9P7JM18EOuZuw14ayNNdJ/HOb9/YQF+3nYVWtGCahicg2gCCjiWKjnOeC7CANXVNO1qzymrhdw7kg6Im8OQ07ufyrhUvsn8iOJgv9IrAd/z6fiU/xkcIOxlQBKusUdYo5uM4aKmrGIjnJnpgBUvqv03M+fGlRqd/79kk2Oo3XFy9GyznVsOoq1IOsefZJEQ9gI2AF9KwcdCzGiwJuJjJhJFSCHsdtJYNeO5oRNXnBNP7v+UmA+/GVaC9dvfsb6hlkdY4qycQKfu69CGeo9rixZAUQnvjABFdS7AhBo2prVIw=="}}
{"type":"turn_end","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign the UI with the design system.","textSignature":"{\"v\":1,\"id\":\"9XoYFdQ5cuW5usbAoRC3/ZNYbI24ptf8dmu0cbRRyNRhzcasNoevA/i4gLP3SP/7541RD0qFn+tznFTjJo+Lcydr/pneI1z0aQpiycOtz2bU70N7+oQHOCkh/UYUwRRFKkCoL7J83pzhNACy83F6nHPl4rb9DmYuMk1bGOOCguhC0LS/vTmYQ7yNchCq2jBp8tvq3WJhVD1Aa4xKriH5Kh4sZ9XNz/ICzzCO4ET0GhNt7IswKPkGAaD+WwsQsJ3a6YhtjYXd14W3bhvy+1/x0LTAnjJ9S43g5pHqtdskU2NBRktZ+Q+BQgBO3Qzvk45HK+gHaNChIkWhJ9l45aeVPZMThj9jw+BZKmQeV8RE/wZY4G52WuMbsaQcvNho087kp078ldf/TmhS8LYvXoegWsMhbWxw/WPr\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":7101,"output":2806,"cacheRead":3712,"cacheWrite":0,"totalTokens":13619,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"RNStl7VdqppAA8Mx9QF1JPr9udtc15F0mYZem+riZ9KHF1VI9IjGmHPb/Y/SLTVvp3q8J0eQRuRYoA5xX343cHZ44rzO7igFycJrxJklgAUVzYsK5PQIbe9P7JM18EOuZuw14ayNNdJ/HOb9/YQF+3nYVWtGCahicg2gCCjiWKjnOeC7CANXVNO1qzymrhdw7kg6Im8OQ07ufyrhUvsn8iOJgv9IrAd/z6fiU/xkcIOxlQBKusUdYo5uM4aKmrGIjnJnpgBUvqv03M+fGlRqd/79kk2Oo3XFy9GyznVsOoq1IOsefZJEQ9gI2AF9KwcdCzGiwJuJjJhJFSCHsdtJYNeO5oRNXnBNP7v+UmA+/GVaC9dvfsb6hlkdY4qycQKfu69CGeo9rixZAUQnvjABFdS7AhBo2prVIw=="},"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. Use any relevant skills in this directory that match the task. 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: 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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":1775941484805},{"role":"assistant","content":[{"type":"toolCall","id":"call_9zX0wo7Vls9hQBmTtDXuyagL|9USPxH4UIkEpEJUNzYyxG3JH258DWULvxNY62wd3BaQtYuBtWLBM8JK2jWAeEoWXxi/QK4vqkgmIJRPRYaCL2Vi5nsMh4pHtT9kczhlMCVwr9gwWa1RLJsFvKJEkOhKbE/jKUKdDCyaVqKngj0GYqCdQn/jsespFNCzlUUD9Ylbjg6nM9X+jFGnXw3KK3NC4pAh0RFLKkoJvpsXm+m7om+L8Pdsm5uMiX0M68fHIK4JQPqWzlBVVvZwsoVDtW4HpWhApdxvNJONhJz1BNm+6oSvjIiRKoYX2hqB/9t5hWwX96liFQcvxcC1wqavXAv+8/D+0OMD6446+JIwxGaDkLLy/3AlGA9Kxu1fghkKZgMc4e7d275mdeMQUgt4bS6WPEmNfVa+PLtB6OUdz1IM518nNnnyzGu0=","name":"read","arguments":{"path":"normalize/SKILL.md"},"partialJson":"{\"path\":\"normalize/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":2113,"output":21,"cacheRead":2816,"cacheWrite":0,"totalTokens":4950,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775941484808,"responseId":"Nk+gbsUlJQZ+eQ2vRIJlyY009X31Xu2PbtEME5sL9RuSk4cQL2KF5f6P6D5SDlyYX8Hm6aG5/vhP5ZhO/yRGuYwuDAnwAG5hqU3N5ycXstkis4ioiAGBnIU9lLK423ifeZqmouAJ++b0/k2hz7QM7YO888I4PYiRu5tQhCRFdxoZl8PfBQpnKqCTEoX+fbW5WaHdKo0ye6U/5U/bRxk14XgwgvgqSccgG6Qasyv7iLn8hVl+diMf+gCs7Zy70Df2xZg9oDLV903pMhK93fxq7/UDflQiU2C2F7+tfKk41IdviHb64CxTJzrqx3277BFnJKMb3fun1941+QO90duYAcZI0uczk/8MJj+48vfLYm/+gxUu48p4K7YY7CriYL8o3tP9vql311qyXZoi7juh4noiePXN4PFNcw=="},{"role":"toolResult","toolCallId":"call_9zX0wo7Vls9hQBmTtDXuyagL|9USPxH4UIkEpEJUNzYyxG3JH258DWULvxNY62wd3BaQtYuBtWLBM8JK2jWAeEoWXxi/QK4vqkgmIJRPRYaCL2Vi5nsMh4pHtT9kczhlMCVwr9gwWa1RLJsFvKJEkOhKbE/jKUKdDCyaVqKngj0GYqCdQn/jsespFNCzlUUD9Ylbjg6nM9X+jFGnXw3KK3NC4pAh0RFLKkoJvpsXm+m7om+L8Pdsm5uMiX0M68fHIK4JQPqWzlBVVvZwsoVDtW4HpWhApdxvNJONhJz1BNm+6oSvjIiRKoYX2hqB/9t5hWwX96liFQcvxcC1wqavXAv+8/D+0OMD6446+JIwxGaDkLLy/3AlGA9Kxu1fghkKZgMc4e7d275mdeMQUgt4bS6WPEmNfVa+PLtB6OUdz1IM518nNnnyzGu0=","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":1775941486903},{"role":"assistant","content":[{"type":"toolCall","id":"call_4UxcGQAvh25lvs95X9QObEX5|bM8Oo3ksdF2FRkVBtN9NxX5v0hpdxQKyYOlH0ItFOb82TvKFDjnMmY0C56quOCl0dEpKwLiskKo8VgscT2wmkSG8K0GxhC+ENUg5yrGw42nMZPPEMbhhXW+cQEjyB1pgJgzSltZebiG8xLcxHc7ppfeUc19F6DPEpRjDIidX4ZMP/hHr5vj+OcG37rFjzk1ip7rQVFlsGmz24cs1iLNRqQuPxBeGICp97VOJJVL+FBKF4A7eNoytPjps0gxHLZVSItMDN0HkIOnBQShl9l+bIn77oDWaUVxXEF4d2Yuqzuf/FOGYtplTl0d+A425u4E32Y/I76/ocA4DJ9Bq9bHbk7PWboaep+2r6RzyWjNJavw+u/Xwgpk1DZbvVeehiiaWMjZeoO5fqUVRxeIVuuJd4XoHsdvqxiw=","name":"read","arguments":{"path":"audit/SKILL.md"},"partialJson":"{\"path\":\"audit/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":3080,"output":21,"cacheRead":2816,"cacheWrite":0,"totalTokens":5917,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775941486907,"responseId":"IVSNVRg6iwJE8pfa8oHZ1IiFNhBWvef6CQNd7E3iqvSp5glFg7dcoDkIlNNZLTkns+Sz8j2xpkbcfI8FzADWImF+zuWd3uOavXfFHhgewY3dcmhjy6N2AHpVR+lE0p0TxKQKLdggjow4L8qYnZowCqGpIaWWXHIeJt2hs3gnFGv1qbU3gqvjr5asU30Y6zHtYmguGhWP3vzHZXt8w1XaTcEDB2vMDzlDvpQHDO13ySslqx9uznW/6KZXeQSuYW+pAMvMe8jg+I6yLb7fcuFL6FE795JBnWXIT7jq4CQ3DD+1xkyfe6iZVZENXIPTD55NjACCCi6fYtycxcbiKeL+OnxJ3Vi80Z3j1ORXDV0fjUwaF8HXCrlFeFIz24mHclGeidmPDM9FBrQ03njIUs7NwX6/XKxRW7g2Rw=="},{"role":"toolResult","toolCallId":"call_4UxcGQAvh25lvs95X9QObEX5|bM8Oo3ksdF2FRkVBtN9NxX5v0hpdxQKyYOlH0ItFOb82TvKFDjnMmY0C56quOCl0dEpKwLiskKo8VgscT2wmkSG8K0GxhC+ENUg5yrGw42nMZPPEMbhhXW+cQEjyB1pgJgzSltZebiG8xLcxHc7ppfeUc19F6DPEpRjDIidX4ZMP/hHr5vj+OcG37rFjzk1ip7rQVFlsGmz24cs1iLNRqQuPxBeGICp97VOJJVL+FBKF4A7eNoytPjps0gxHLZVSItMDN0HkIOnBQShl9l+bIn77oDWaUVxXEF4d2Yuqzuf/FOGYtplTl0d+A425u4E32Y/I76/ocA4DJ9Bq9bHbk7PWboaep+2r6RzyWjNJavw+u/Xwgpk1DZbvVeehiiaWMjZeoO5fqUVRxeIVuuJd4XoHsdvqxiw=","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":1775941488445},{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"9ay2RrjHbfOtWq6v91OAtkw4z/V94btMMNFYva6RAPyAhlpBReva5c3fjw4HWh9J8vkkiFNmUuTV9ebKdNU9dITpsjDvSWQKRIjb7ulgpPEGDBhZuSVQ10N+5naeFQgzCPdBx50el6aR/b4yw5vCUqdsRUHfXsbURypM7teZXIaPVedPI/zTyA2teJZwRXs1s3kNIUGx/H5URkSO3wMUL5mqRtK868EqikMa04BeG8Xu1V/c+UoHMjTQ8+jIr1T9pYLbua3n3jbFoPdR+TvoJW1WlGCy5b5ikJnO5Fzb2HqUWeTK+M1u3LhiDcZapyReFs2kAw6pWGphlluV/pct7WPorbrYCqhza21bncJUhg93kqRU/8/gdGfSEIY/k7CgvR4dxaxwZaigEzgI3UrEkUl4GfM9Umw=\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_7gQMmqmcbg0oWk2txzMgz116|N22ACjlaPRGxt02gQpgNmQJSeDkWxHzzkQOGuIdh2yX52PIyubIV2/HQAW595wXsryTKS2S2371/8BA9KCCVP/x/fufqF0tod+IVTy46RwODJKm4OOJN8Nbr71sLPYnaGePf3Y5VlZhvgwj5yBoW5M4z4ItvOkhHc/8VrVnI8edg1ICAt2IRiVQ8Yiq9g+pSi/WF1JMCb84MNEdrvrUsUZ0Nn7ksxVMJLAz1CfIjdvSTXaFKtq9wYqMm8jwgG1eG18kEIiA6qofmWYbfPrqVItzq7UzAoUukCTvbV9v65nLYl6wwBFR1F7NFB7s+uZpNLYErgar5iUBY3N5lA0T08qQ3njqBmZh8O1Nfe86qgRuTr4CwkpXzzm2cIck5RjpT4ZlKdVr0TmyaLxVXx4s5vVxNCGo6bHg=","name":"read","arguments":{"path":"frontend-design/SKILL.md"},"partialJson":"{\"path\":\"frontend-design/SKILL.md\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":5431,"output":60,"cacheRead":2816,"cacheWrite":0,"totalTokens":8307,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775941488451,"responseId":"yVTMgDKHLUryZhJ7Rl4kl7VNOuoY7+PfOhmHGlmJbpQtWRKbFFTpaFhRfzQUg0OQm5l8+ddJ7PfFCFTjy0QJG060XRTZjSJCixd+o183m11ZRNi4SE/MGsEawz7bxMuKsdeAev2M56+5VzIsMiSE3sJJtZg+rXgkwXx3SvYXTvbNSseCag1x6Z8aVqfkM7ke0na+CccJtZOxBJWincfp1E23IePaFDaXMD3l5HllqRLX741sPfCICRezX9It8sBvh7PaAqH//2uTQpcnOIgyPtzSwKp1BMW3sXZ75T+mk8hXXRI20LuN6wYWeklOB2hpE03xWT5jyT5ROxqKRM3ZUEWShDaFWkiwhYIlJOR3Jhci6gdCYzO45Tty1EfvRvU+f/4MGUiQGvvKrXqHoYc86bQuHoQazc7XIw=="},{"role":"toolResult","toolCallId":"call_7gQMmqmcbg0oWk2txzMgz116|N22ACjlaPRGxt02gQpgNmQJSeDkWxHzzkQOGuIdh2yX52PIyubIV2/HQAW595wXsryTKS2S2371/8BA9KCCVP/x/fufqF0tod+IVTy46RwODJKm4OOJN8Nbr71sLPYnaGePf3Y5VlZhvgwj5yBoW5M4z4ItvOkhHc/8VrVnI8edg1ICAt2IRiVQ8Yiq9g+pSi/WF1JMCb84MNEdrvrUsUZ0Nn7ksxVMJLAz1CfIjdvSTXaFKtq9wYqMm8jwgG1eG18kEIiA6qofmWYbfPrqVItzq7UzAoUukCTvbV9v65nLYl6wwBFR1F7NFB7s+uZpNLYErgar5iUBY3N5lA0T08qQ3njqBmZh8O1Nfe86qgRuTr4CwkpXzzm2cIck5RjpT4ZlKdVr0TmyaLxVXx4s5vVxNCGo6bHg=","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":1775941491338},{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"encrypted_content\":\"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\",\"id\":\"1k/n+9cP/rOCYJT8ovV/rChntYXPmiSh+7TabUp+Uu9TbmS9rwca4Qkc9KSYUfPYKtZvQw8bFp2as4VktmrgA6OOBQpYCte/K+yBjPIr2UkpL4zTPKiAsFXe7O+K1PLxCKOEIxv8HlOUo2b/0Xg9lJKSbrKf55JT4oEXMWQTZYC55EF98TcBWF2vQZr66wdXzJGv311xvkpPbsZKxTXnVcVoPxdTgeUJQMtZNIUoHwyTsJmsnBxjgQL9FEmfxqFZvjGlZ6wUxOE+mlfR1QjIEeiUZNqT3uqtIhcs+rhTEBsFrZ2TdUO4ayAMG2+f95S3JBanfpEssq5WTFFza6K4kAIojuqdDwX5mLwbYRWMjfBSNUvvBkma1D11u9KqLD9MAoyxSLHbY3kp+fclbMELXfyDx6VVR8U=\",\"summary\":[],\"type\":\"reasoning\"}"},{"type":"toolCall","id":"call_FdbBD3Gla4MQugwCUybMA56i|TnEaFJ5RQHMQTst3nmwchk9MaGOeKnksSVeKNwdXvCodVjz8MZN5wbo4bstNGUnMhB7SLiIgtNED42pdVAkGTwcw2IDrhddBsQyelsDA/pnvN2tSUAaJwYCVQHMbV9gcJ+vxvKq8fEvCaCFy92h/oTfbEHe+FKS8TFhyxAF/WXylG0oYISWRTzVs4usDn2qoyIU7ycIV70sUCattbAfqOiWoZcWoV/EalPKO7vfPcp8Lhybu6Le+wAyF7D0vw+YAYzaCj7dtmrVuk0io0nUfetwhGRHVSqz6UhS4hWvcQ0fDlxsgXaenND2l7JEpFaiwrny+GSwMOpUQHP9QlRa91oNdWIu5io+WmGAJbSmKl+oLgFfgaFWadUVbL/KZzIOdtGA1xOb1XJyzx4IA+49WZdllG+FZgp4=","name":"ls","arguments":{"path":".","limit":500},"partialJson":"{\"path\":\".\",\"limit\":500}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":6856,"output":169,"cacheRead":3712,"cacheWrite":0,"totalTokens":10737,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"toolUse","timestamp":1775941491355,"responseId":"3FQi6hzXlGcwig3CZtXnc4Q6PD52nlvaQvA7sNqvJRx6KnUKts7lN1owuQSbenzg00MQAALYhqpV7Bpp+f/0JIc2uGoQ3Nn0/yiptafqtnazwhXPSe46VUFDCl/QIjj8wpkZ1Zf1dnOwRuvx+dcOJ/8DHWqqB50W+AFQ8BlkGVn1c3DlwRZ0OrmPTvm4h1SGe4P0n8VtAgQ2e1CA1TQaAst9XIqNtSdWc4C7J/YUByeh8C30ocMXiXWSSgyjgPInpsLXwyoCSaDPNtNa8Cnoywe8t/+bSUzFH3AuKR7NYjnPWh/G3P7TAZFkzv2o/adXFeGJhiKYPUWgziVsjvu5iy/yfP5JdJwuT3uXrt7nxKWwGrKZ+U0MUF2M3ymBK8RT1PZ6ENqW+1bK8YppY1mquvWYxXZiWvqa6Q=="},{"role":"toolResult","toolCallId":"call_FdbBD3Gla4MQugwCUybMA56i|TnEaFJ5RQHMQTst3nmwchk9MaGOeKnksSVeKNwdXvCodVjz8MZN5wbo4bstNGUnMhB7SLiIgtNED42pdVAkGTwcw2IDrhddBsQyelsDA/pnvN2tSUAaJwYCVQHMbV9gcJ+vxvKq8fEvCaCFy92h/oTfbEHe+FKS8TFhyxAF/WXylG0oYISWRTzVs4usDn2qoyIU7ycIV70sUCattbAfqOiWoZcWoV/EalPKO7vfPcp8Lhybu6Le+wAyF7D0vw+YAYzaCj7dtmrVuk0io0nUfetwhGRHVSqz6UhS4hWvcQ0fDlxsgXaenND2l7JEpFaiwrny+GSwMOpUQHP9QlRa91oNdWIu5io+WmGAJbSmKl+oLgFfgaFWadUVbL/KZzIOdtGA1xOb1XJyzx4IA+49WZdllG+FZgp4=","toolName":"ls","content":[{"type":"text","text":".pi/\nadapt/\nAGENTS.md\nanimate/\narrange/\naudit/\nbolder/\nclarify/\ncolorize/\ncritique/\ndelight/\ndistill/\nextract/\nfrontend-design/\nharden/\nnormalize/\nonboard/\noptimize/\noverdrive/\npolish/\nquieter/\nSKILL.md\nteach-impeccable/\ntypeset/"}],"isError":false,"timestamp":1775941498355},{"role":"assistant","content":[{"type":"thinking","thinking":"**Considering skills for task**\n\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\n\n**Selecting frontend skills**\n\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \n\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\n\n**Choosing frontend skills**\n\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\n\nI'll format my output in three lines: \"Skills: ...\", \"Order: ...\", and \"Why: ...\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\n\n**Selecting skills with steps**\n\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \n\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \"frontend-design -> extract -> normalize.\" This keeps everything organized and aligned with the instructions!\n\n**Choosing skills and order**\n\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order is likely \"frontend-design -> extract -> normalize\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \n\nHere’s how that looks: \n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\n\n**Drafting final output**\n\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \"frontend-design,\" \"extract,\" and \"normalize.\" \n\nThe order will be \"frontend-design -> extract -> normalize\" since that reflects the necessary steps. For the reasoning, I'll say: \"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\" \n\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\n\nSkills: frontend-design, extract, normalize  \nOrder: frontend-design -> extract -> normalize  \nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.","thinkingSignature":"{\"encrypted_content\":\"LElKq9fY8CyU9HssDOv33EsHiFJrUknrBQonL6f33JXXDPb1TUnwO+uhz+quQp+kdfc4tm4gQRMxLoG7k8tHFAtj/17/bnj45NO8JAGgtUneVlpSpCQopg4Awtn8FC5hP52gqkFuIHosXKicHP3eyGx8bX7IdRj37bQouPqOtPpORVozRhJD469V4b1/cTFpss5gEMB/zRShsBrcMS/ooPtEkZnk8eMxrmgpQD/SzX2LkULVrMkr9EKvjwLhaxxYHngnkA5d40MPu01dsafCGymOOwTyDuVo40N3rs9LmowVAR08RR+OjnaqKv+yoQtAi6Dj2vyXMDWF0OBXsqDo2MDAsb4OEETRN+IaKFNPoRc55WxwxL1SL0Wucvd2dsSAB70pZmPp7qMy9VP8oLCR1S21RwrJxeHQdgw/7bDfJNijr1NVzkDrBhI1K5RpZl++Z1/fTP3GqH01ESV/XN88rXjmtZ6XJGhteRr/t5B66Qe4BC7oQKl+LvIeUUKFUX0nIJJkMmjuUXl2/uBvz0mdlipbCLxfNmbAkxge3sSsLZ9C8vtPx/lC1GL08TGu/BhEB1ru5VeuuTVlH+8fWfuhukr8iAE6wKlPjuyBMyp7vS6+wvU+Dadz/OBJIraI1G6mNV6ze12SIDgRQu5XAWdhdhLnkDc3lVFkCn09bMGyGwzkSRFpfcCj14iqZ+WW/RgA4FymCxQyVnKL7jl81LvKIVC9ChMBU8pt/jzD5gqe2CsE4wB9+dZUscXg1yAvZo/e27TdNYDSyUwiG589ja8plRtqGxImAIQruQh6pZ558RKSqAbw1KYsJqLiP7kxpu/FlTbqTzSRdpXiPeWSSJGTfrW3HqbDaRE2KY6T4Y43lEKB3tG+cQKgHx7Cl1V1fH7YieRcQoBbGQvuhATha9S0NQnztmcgaCKM88v9Sys7Z1KmteatGeHNZ/qwhMfUjWQPCAQedTskRIwd7UpqcqMq8L1vL2DYquRZ+z5urQiC6YISJtSBVqH7thMlQPnp6rao0Ffw6Si/qFtfZMo7lrDWHtKpUz+M/4HztXUr4hieN1gI7XtsTikDpQQVOYG5Xj+s0BoHElJk9WXZlilBB5JvQpoTjS1zyA2klMtQukSxnCIiLfN5PxkKl2LLYItfFnpfQ21c6x68adaXsxQw3/2UUNLghUEFFtg+LRpdI8nG5JF3d+lhFTpx0VVaqeGUhldGRvkq14KATbpn1opnnJ6a64IYOJP0Or23ao9qDY5jnnXexJUtimOeTWz0xT1y3Q4d0IiV8TLC6lkWnueRgnoO3n45JIQQHQpzn7pe9w9rzoMQvo3rlo8veNsbDTRmFiNm0GMOWwdGlR08qzxYl0EvNfavTGtXxYDqPf4N7GITN2jj+6BCk144gH+z1HlklevNkQqhsguHreqe19x3j7X6KlK4GdP5WQujHTiixnVZtB7mx+b4Hdka0C8fHDl6Py8zzkQTvYvs064yF7H7fCPlXrfuFMwczM8VGVa4ayElG37/tmQwnl0jICE6ZJLXJS1Rkbfg3SkPl1Re3vZiVSRCpjFcETjkBzsQzeUyLSHTHy8YvH4pkxyM84jVFco0heyJpOzlP0gI039n4NZQSWvG4xpsDT+VORtZN4hbZZQ1IrXiaeOKg0quEBw9gcYmywDDhfpnrIr8Iwne+FueTXD6vXCLCQ6FwufmpUa8V4jMARnobO6gC+/ABxGY/TcSVspozjBg/98mcZRsSkfMNIvKdqtIxeo+YnSNbsSHIGKBmngRusPrs6w4aackcY+gtapGI8vW7a6DSeigr3lB9sPkrk6CnonnT672s19T7veHQETWfb5O9Lp9CP2EG7qFjDIrd6N04k2OLNTCD5k4dwNxSG17IjU8FxSKT4Ffc/Q8jn4P+GFkj7+81+dWZuy0Cl/qJz3pliVxOuAxMU/4zmUL7zdaAFGhQnw5hqfKyVq5rjY12bUbf0oN9ZFv712j4KkxgMTKQ7hDw7ell9xCGCuA5KVpcVSfT4Rwzr5q1mgy0Jgxw4E//KMKjn9qqe/5sXbzIQkdbzy0wEXZBqbHKwtjAx+sxzWDtANGZdAu2QmE8BuhDQSBqot/R3QRzQzx5RuixrePld1lWflWgkaBwLvKcmP9UdnbXPckL2a2+/LFecgksCvVooN8zyiH1W5YXiRWVKH9TE5x2cI2X7SeMRshGUzZV9yfLR/RKMLTe329gsrWLsCcGjxzFpywHhlt7FAIWoiiRCtMU4arzj4kJjKz26/KKCGCEx1OgYD4GqY7IDKOriMOZuSw1XfXrhxKtU4ddfqXI+3gMxSS4Y03mc3yvonwJxzd1qXk0LEOwF6gj8xoXkpcyqoxNr8mSQNPhJeWhIFg1mlyyIRUVa05SIFKld1qc0AMk1n8/tCvzHpCbFScXL6o3o5b1EJetyGxdRmppr+AoovtQ03y0G2Z1uUR23AtbxkiZWlCB+g4Vx5eIm8e8uLw2s/AG6Qa8cPSKnhgJ2b9i3UKpVS2mHezLGTKHNsEVGxjKoAdLDrEaj+S6y10J3RdcQ5r70YuMAQKoT/NqYjiE825I2IvS7A7W6BIMFMuf2sPhCpT2RPtirK/ppC+Z8yGrW9cZFfGeHVTIetLNP4sMCc4R0ZBGcyBEv8gXFxRyQ1WTraIWQMJeMDGSfPwy8kwcammlgVyL53QGU8Yx90hXIiaUX+zbAmHjzk6T+yL/PrD3ed61A/eeyOwcWq0oQk30pvtk4VBeNw9vopFDJPJdoQayQREDTc6u4mGbj8luQlNtwtxaFzKTKjwm8ycMUxzGPyj38UMeuZePcJ3EAeMIAULi/3p0LHKnmJQliIeCMYZ1INd06BRUaH/TxlIICQveurvXIehKny9mq4DDMKStEkXVkMkCg25hlPVkEvHUB24FVjp5emIc2XCpJfN0jUzM59ZK0EepptE/CC5efI6I50BMs/ssxB/5mwDprSwVSHnHlcFZNgCp/ecTyouhS3BxwxfKHFNSEBkTayxz2i7U+GLA9jQ4C6a4oqf8Vq2KM7i7ShwtuBSZvgPWVWl6Vu5epxEatszjkdibEt4HtpcGaZuTdCYR6hUAWYn8IA2BqWS7gojfaJRV/jp0NDYXa9XvEvE9zLPHR3HBQLxEckUYyaoJk4uYeaUbs/ieR594MeMI7BJjaOieu3fXZeZozi+3lREEcDMCIvy7mBUaCBHZNcQFuqhRkRSA9X4CTy/QH0E9KfXkrAchjsQHB8X21Bql2GiWDZ+cthlVnRGmLLQG9/WwwYCrE3/q2T1aBnA6a0GUmH5OcDXdM4hI1NwkjW0hbY3NcleOh9Ad3LQ30Jw+pkEo+cPggLLwjKqUUGwpVM9N7OV8UqobRPP4yR2pi+57NKPBV+0iddQfClw1Uv6LubKBX02uRm/bGCtLqbrGTIlHDJ1aVIa9QoAon0fKfhh7nGAXxO6zIevC0c2UhttpiQEXO22i7orrmEaXYXTZSPKA8Z2BFlUlaHb2WDe/ox4IjQpQ07H+kaYT/Y72j0nrIJ1MYoBQfPToHd1hQG6wMCLZb0l+HwH/FxvlAYwpSj3u0V9D5wmLfVyadoLP4j7b+Xgd/VbR9DXrwsUskOgZEWOOs8CdayrparTSkQFVgYDDaMqgQkNqj1e/0kiXp+RrcFsrNz2r+icUeZ4DHbfrvTB8ENeQwjfpY7e8GmglGtllRAuSPxzuEwx6j6+Gn9hFehSfUNmO0ZPU7on1N1SRE0AqUZSjijRkVxXUkRAxmhg7GIjeP7faySGAGzViWCethRbzThYKDx5kXKjtbebq2wMbaAIamY/5XQsJmXndySfoATjUzR+Hz1ehCFvqWLuPQzr0pyjTindDIWDHwEiG5jn0zJ0NLcYZ9Bc2px+K1+VG+8vpFJPQkFnyLHCpMwNr4ZiOorfYQx6gbLy/AOSCcVEQJkWWLIh4TEWeuLDxYSaZydnwEfLjpjXgbrPi96ZlZcbyULIhTAqg6SFiXQr7W63dnJKzxrwA6QoV3TYQtGwRL2/03yk81+I8waZBFXNLjMABDY/KMc0VznMOU2HWIhHUpkUWcmitO+z2tdSp0K5CBtqFUeQzxHUllSuEj3J4ZLRresIbhf4gXB1eyIKPb0TbPbthVUDG3UVQQrChJ/Jz+E9mGRDQLtN3/I5IA7gqsqII1dzer78KAktdAKEt9S800p6puqQazddq0/Q8Y6ycVjJAEYshQunAjM0ev/TG4j4jjrZjjegNKfUvwKcHzYyNSLNSbqcNRzirL8Q1cGDANFsj4wl+lmggCD7p9G++uwtsWZ+iSkjzr8Q++A4zFwNB+WzxLlfSuQ12eXV0tEXTm1MW4rZ3IQO13DxP1s5m/TgjC7g5NlsdUrprBqvxSeK9ThHjRg+TKp2bYAiqaxd31bvbzu/r00QW9U9Tner9nXn7trZDnbfGRWRypXPCGTTH4u39vgO79x7mAglMMRV5txmFcgcgGAtPHF3Fri7C9vZfzNFAUBc8AWrAoVKrXlJ1bSelEu+j91ixD5OqZgP6upPQLTgASGArpU4AK40TUDQnSl4uy4nbTLN8RQKa5RJYvIvGXOiFao/1QYczOZWAS21BiEBcvmqfN7/MsjcUwjwMTgt6XfSGbGGukP+tusVeHuKA2XvbBULQfYZpIiL8fxrOKvYBTfhtRQB8IJdrYHj/+eMn0JbewYMQ+xrk2eOYqxTMtImotpuA8afG7Xu/0XkyCVBU1ZjFkpARSRYVtl1Se8RWMRzb7nyU0nmOV5GS0VdupQ8Qz02RijQWDtp/rAwlHpFBT6PfnnGM14N9oFpTc7wLrLXa4W5qlfpGeuinvmP54D7wqSr9G0ITNqFoilyPnQvDGCv2wnD8JN1koyOAT2O6SFrkdyTrD6NBVG82kOdPCBpDk5IYx0J5Xq0L8dIw596TwK1khVAcdBG4z0bISDg/ClFBhVrwHid3QX4kKJZA13V5B5tishx+6syMnkmowtM6w9jQRSxjaGFJu5fDC2SHxMHzsAI0bArNl01J8c3jZgCSfn/PoZy8fmBtMuYwsvRxRUFNmVKGfGnvcdApqVE9Fo8d9iHK/tM3UOSlmKQpeb5j7Komnx2O/By2RUzKvSCoJ2eKoRIDlcSQPJvRM9I8Hz74st96mmGwmhIqqkrrZ4Qfe+svK5mFcsQhRQg8vX3vNXifRa8Af/uuGksDfv+QtrLjGnZscmMp9H3lJmO++jyUH6Vy256tDjrSUjK2RBatgt1FJaPdYpQL/OfgzpMD4D980WvC4R3HIDpdtd2mRP2ZttmjBhAKwsbnAnX/+Axvg3eVnUdB8SDPmKLPibT2t44SW2WNZkIwhTH6CD0Wj2xzntFsBp9GJW7gAWWAymGClQQsKkbWFzFiTly6h4QW0Zf4se4yQQJymdfyAR0bzgkH9SM2OttGiNveotV+KcvanThl36/sNBm0MFSp0MDQvO/Gc5YMaZwvW6hwe32v8+c0WAMXrj/wnN2g8CrPPJ6P/GxN8rCIIov5TuTMD4yLeUb8t7BhEBPmL0YNWG41VQMGgsJTwzN50QNz4zkavfikQ37ngPt1OUH+TRVdXKrvwMQvD7O8u91uvCnU8YQzKQkQwqi+JFYJEFow5oVZlrPcJ76d16M5StLuKjCkZ8/EvN5+FcKgLuRK1m6klX1qKxLG1EjL3ORFbGzsECoHqwfzVsV+lWwQTVrvWmEZ5/CPiR3wPvPT4iNIiHwAoPCBip+AWBOmBYbyeMiMvC4GcJwgLAcU7cqag/JlnvnO45qXKtgptzYfilGUBLIdLzaYlsmwbXiC/8P7wof5rwwJnFaqnbcP/HpV7Q9sHMR8dGzw8r+n0hzK3v3wCvF5NO9eP5T8sdAz9ABO7TR1wTxBVvMqr6a64ylrs6KMwENKkMLQhpjJsh8F+qqX14ORJdUPE9R6w7NZKpp4VJnY3j+S/aI0wHl7c1dT20mrrKMl/QoqirKSponRv/aBnLPmm5qb8pRdC62wkDLsyz1EqJzML/Dloaqa1balI5Fkr1utkcqIyewK0xB6OSKlNvfq32bp9HjQSn362o0V1GZL8B8RMxy41bDpbNcARWGc6SrX/ODgEuy27wGnmTlLroJvqZaRm91CFvqY6/XcuJ/nCafDvE0dpNEoegEJXwJOj0e6DcqLyiH7nY21qElokBOTaQ9WvAonGKMry6mASll3//HegHO393Kpvhsh2oKXXOQ0omF3lm67pzB/P3qI6xsNQp4a0MBTyfda6yhHjxTzWYawqIJGK4xUCYAIS7sXrDChUmu7DDFWadWg1s9n8Y4fVzwGCpbXSpJ0j+BRrEVg3t+ZF/IZeJK31zBRUIOfaSTV+GjPBQyCRGsppF0KO9u0456X37azvIuZZvhV3l4dTvAFXyzjTkodJ1OyNCbCtqMx/zBDbOXgihXPqwGp8i+NI8vf3w1L6Klkj1Vg2UfwLO3tI2rbfHJLzv0/VZOcmkEsWKwQm262yFcCaLwtNFdZ/HJEiiHO+Sgy0KIh0PY322Ne3T0uuH653Z2RsvQSO9L8c9GMxkqpRc/FDKHoWLGiXCMOfteTuZjdKtB27Hu699ZjRfbBj5J/2VvkncJGSw7Upwa3pMxxN6UtW+LuR/DR/2079faUazpx0JFy0jsoZj54kKPiJHIuqXIK9u0wkqL1JvCaWkbINtZCOtreFxxf5t6yKEn7BftqTNpYXud64lf62b8nOwLjAOknqtgY8HQsmZQa0lZh1FVL0vHJgJxCmB48vbgxXZreEA1hrf2VNPAPTrQ82v+UDXTEKTC6uR4f7mQulhGknKH2r2q/h7jOUQ2QJgnqOfPmu69q9lsMpjcYQPQGVPcQAqThZt6K7tYKucLqQQKBMNwBgOHDDwPqmCKlOodEP0WYHZsjm1bdFEAWaMlRbQnI5iAZynXHmaGWX1I6E+f4hzkSIEQlLELp23qKl7cDA7Z507MSteCir9Q6IH8B/u4zG9MvRtAZS3qfiEhWKpLAl3fllEvsmDfDkHfkiL9fRQCCfMmKEzJCG82kDbr1BJ8BMJgga3yxMGNwz9u2HSyyV3elyUasblSMdQofiGWZJO8tI3RvfYjBMMTvzoiUBR1KgjjYD+arhjIw46NyYg9S6A2X+Bmg51AiHjhZFl21Fcz7LZLMp+1YToZoWT8CcfdmN15uIqg9zU3g2NWrnXViM92smDtsCiNKle8teB8pKXLn92tTkDza6I/+C27/GL9EtWnDykXt8XtHhGQO6y5fVWDo51hXM9UIuS+wx7NS6plD3YRvrRRq73wyBdtvdNWZzHPO9q1yW6HEsc+wBOLldwFJq9r0vOAoctztp8UiXB/cm30VEQk2iIjsngXJLbbDtKLM5dwwa6kWnY2NmdB0pBUlt8MyrkFiSUGnIelRFGxUTJkZ33AD+cmQyBxQxKg28RtFpssucCPe8xwB/iS6TtwFJZPl8gb0zpDJxbqkUETN5HaP0zRgJ9EkR70fhO61WcSKpqInCOFMRvq2g6Pmvimz9DoRvP9cMJT9gnKIAAmdOPiQLnMb03kc1ibjwHTcE8DkuuIWOUM8r2S2WLHfS9wbOMEs0Sr3/0X9jhEOV3yTK0wOMjUQdp5WF9hoLNqIaIfORA4zeOegFcv4BM007QZ1mmK89N63954c4JqfII5MXrgA+nd/uVx8/ySY4f891PlspSy7yr4A/uyfrD3zzzCjD8OHJEuICUGEu5QBuJQtQ+l+/LFGJieyIR6ED+DZkT23fT8aI/unkntg+u5dP8WV9rEl4dOtuVTZR12ub33RsBm/219PO5NC4kfamFT7w5f8+ibGiqgi7p0alZD453sWM2ErkFB5Ze4OS24RghTQ3NsEeA3Vse6V2sipfdEhH1+PrnE7lM6AkRsutBH60EQ+EUdii8+GmpCLFmg8eJYxH0ugDz8oNcgMqsrwhWEYMx0iUtuCWdJfclK5Sh9RXWIic6rSY2kwFzFWOxvltONdz7Pd7wh5vNx93sO1dj/SISsqk0+yYDvIf++/N9PxLZYeghS1+Yyxh+8ePG1Xw6XBnV2T3uqMGfVHJQzzTPZYGZy0H412A2RtvBLV7K8LHNB8XA7Pz4l1Dgk2dCBFlNgWdkx1wxnpaJ7oKgqdqDx6tBCEwmN35mH8lP3GoQWbibNToFLhX94GR0F+u3mpPs8yZejLckfbttrpka4a20xejYHkDvH2tsTrTPixhO4lYokvVf3C6njbq13nC3VKMa3eDdLZr3JK5VGkNkvvjdzBokrLXsoATQDfYNNvbEuKsdkrn8Z67/fyRqy++5TLhd0R0k7Kcs9UCgVCeSJ7HANBgAbe+iXZ/iJPFsXjzR0BkJOlh044qC9FLtmdJmGBoFgM4vvdQkw7fCSZzYwGCV6JjfsSuMvYjsOEzZ6D8aiHs8dNoY4UCt3f0lBrnFyu2fKAeVsN0j089D84xseWvrehusj9D6EpbEXOGHBY4fkaEtyp3GwEByV09AsKjTwwqwCPE2X37t6aTIq8xIaWG47Uq2K7ClIQISfMiQGx8GzAVINzcZTWkGYEfMlVPqUNBXlxCWnZlOMl7JwVZ25woJc+AKXQnbVhY8+HopdZKSP2DUDctVwlYLbSIDpQU6RZ2VeTO0vigkJWcHJt7eaD++3CHi3tyDfdMGBMxSa6nlNe/obJbT/wl/HzASsHVVxRVRNhh5NwkKJ1EefnhA2en6UPZJn1i6SYEOz647j8P/VhJbk+Z8ftQi0/1hGXnhXxZc8yfnVCysawL3l/Hm7sycFGalT19cxBvWHhuIDW+rjjyzxWWPn6P2HCvCDTyZuAUyTMaAYDOE6WbKqSpEPSg8ppgj2OtLM0N/hF4VtfMB6bCbU6R6o+2IVVItRaR6drZ8PbSiQmby1bvgpRdbiv9r8aWu3D2n41nKpj+5gaFklnAN+NOIgFIRDWTrjLEWK1+Ys9GDs9kKdPXEUJ3ME0+CAsU9YjYTizE95cNVQDncPsJh/xhg2mSLcyM1fDBlQhp8kmF0qiudVFAg8HHFEFOEDuC4i+AHTBG1297LEXZQmX2Gur2kjy7m1Rmgp0gKszs8//lTiB/A8SvtFFdphiOWEaTRHndhbGtu6GqtoTMp7vUN6KrdiUDVrNm19PGSiTu+s/E1ankxpU54zclwx5Fuo2OLxH9JDRz3qcUEWz8hdR+Rz5WHz4y+whS6FmHBLScUgFum0KJ4lpDBfsx/DGfoTqMQQDM/53OCu9fQHqUUi7zw6nTGWBJv4lR6/pVSHltS5vNEH82m78y1A/Zdj3W2HG1Afm3Jwuy3b8wXxMWTRhh6YEZSH0njXmjIjK34UOr72Zj7BEdBpqeV0IU2fIR7TOVYR0KeIGivUQFB9d/KYBcnInzp0ZYg6UwR+lY6Ji8Fxs0eE7yVWChtebpEv3CiTKn6PyN86kxI1lNYP0F97fTu7B9g4JmDDrKXJXdAGMEFa9IeTxznSiQO2KzYmppAGlS65hVMZnqb7Q8Lrio+sGmjgEiczvg5+uWtuC8jupxZ9CVwr7USm4tLx7DoKbt+563JNkqOcb/rzeKAniqjvfFY9doB4IGqI1Id3m8ycqbTXdHXDp1DazxqFMp4LLZys23JcrEng8nDn4Hhg0TShRTzN36OIhPdXchBeQD7asHBkNySehuXdT9WWjS3XozpSGnbm70FHpReynV3y2m+E8Tx3CgP3dELP1IpIjgpU/N4J65ZozKxoJBeM0hRMy5nchuzbLw6BzEtHUlaEOPY+rNweINc+V5g6+LFTErZWXXn3rR35pr7ar1cYL86xp+IDzuWEKyA9y57tghSU44LBewpclIE2nROU6l3OznIJKKC4/K9lVEvam8qsO6jjpUkFa/qjThzyHxDR7Wz9a7y/MHLMf33EMx4n/b/Ny42v0GRFUvG0rIUQLZ+nWwMh+zqBmoIBA3tSRJEMR7mJ/k/oLOo20CvJwRaMzghAjk7nyGlZOC5Hy8Dy04jWcA/C73bFQBVwR1lPgvYYXMpd/SWA/YxJzgjgcWT2nqZJg8r1x+TMB2JjuobvImVjUCzaMmGMyRzMKRvq856XzydEtAexZzOYse6r4weVfHuG87dvqidoF3QFt/P6KpaxQ/EkZnA8FDwjg8YrMy4axlN3TTUI5JIrDqij6R4cpIGzPSik/G9QvtUleC6l0r8xexhTO4QKCX1NMnnQVuOlOlf8nK2aDXTci8c56Q+NgNJO2cYK52WU1xW401iNmXOZX9ZNPtkrUIwXXyOD/Ms8TF4Oy5IyHI1e3jZ430RQWHcZAkroRzAGngeVtTfdhaiPyyvatNGrnGcvt51ctSqLxtzR1P3YSM5hXDeZCwvLfsbYBz0ooOx2EexVkn9P2YOXMQV5ie82mqEPjj39++EYhfnulH8yOQ9+WT8HzvhPG66Lk2znAZkPAKWG7i49kIyIAO0D0vCR0BQvegXwWw7gDt2yKnb9YMd8/HU78rKPkX37U7jWmL/kP1NoYx761GO1iSBYgN6P2yWOh8rIZQyYCgyuHZG7X1mpVuNEVfJLvhyOcO3/nmOP2ckKR7AbobA4FGdmcFkXHJOLFRTvagIESdYdtybJxPqdbshpqCicF2Fz/6jWTNP/G/pXoc4iNkzIOb82iznM/ZrF30Rhzed6qiYZozeIsiRS9/VxYF7iobX9L/N5Xa7p3YeWfTN9djUcGFCLNpqFGoctc8txuM+mGRDedy57kDiQRb29huICuPuhzhexiqq5vfZoslNRJzRF+ZrHSA1NACYLiWj9oYlDJmdyOU36mq9cUchDjWcgstnO32RwdYK8FCncqvARnpLinqIGGyKOxrFXM4VjkdK0O9Y3E3drRvjuoQjFjtXmEmQVu21t/BGdBaSDhaisYxswug3Ncg/O4d5guw19FPvKPkoHQyJlLtBJQhMblWcCVAvJjywo7Nxdv3CFRT52qJ8yOjJgppMatIwbgd3igAuBcZWrfRDUqyG9rRTaiAi89Z700bfRLT2pydmU+Rukr3qpyfmGqufRPtWGzTIv3qHn/j0lgQgUTX7K1TpL/bdCf5kQY6Eu3a28uUS8XRMyD8NmILwCqn5dJXjT7lIaftjTnR2FsKYJdln1lDL8KkZZNAJPArrw9tL1qoABK/dYw2O/GTe70Sc7lZ4noZvd2mfRhQMBwUiuF/HffDSFIxtHOe9/IIhhCfQL/sdF8bHphdf+mjhVdJkFOezSDlt40GHwevXkEQv3FK6txMgsDLV17/lPh9yccrLA9FTnDYnjEFwZJnc7JrVhekdU6CqLUyXBbiCh7gomJZn1hcTdFmy3J+JNmqyDWxcwyMadvVUaCcW7BJU4VtGDsWi0QDb/dWc5hNqFe93de6jhFwAHFz1ewtIkDcZNcjEEu2xBpvnys2lOPuRp22xqKaFOEcfCkIurw62X+/jiYJ6B/e3YjNVq2KMatU2VE42bI9dnWTFS/oDL6pkZqSN/NcH5esVMkMjzsE98FbPk5HH3R5eZbdh2xzxo4NF9qQMy5bsfj3F912kBVD2FwR3S9RPpixM8VHygJEYTuWoY7IhMkAD1vanMH0HGqhtkI/IneDQdXEzh7UE3pG71/Vo9pf1e4cgva/HdnevwnkN9iZJHwcaHCwsIm7eUTZC5gXR76fky762SWJrn/JsOmduLE2rSXQE7mnFEHzAg9LsPqYXlqGBQDMSyph1s6lhcbJtdKbucsHskif/4L4edaUkA+apoIjR0kEUuGJOOe+QDyIy2f71quIIKy9By4GktHIagcMn6GHX574/g2fqxMw76O7q88ZfDZbSN8/B60UjRb3cCari2fl+0/mFltVuB8hvWDcBoCsl55TgjfXgnG830h45pY2sVwcVPbfGq/8Y4ZPdHsUyHFqi1N5o7qNdcl5eUl5wXPFsqiYlfZbAAnL0FIUx4+E+uDBIofyGtdgWymk24r9zb7BnwUY3GuuA3jcrcNFPQtvSSmts75x9yDZecOFrTiWaHQrDw2fnQ8SrgLpD8+lnBEKmX82+osYAtaeXzJMrnwjd1LqUiNL+icPXKpjyWHN7zPYvW1YtobdR9REaEKckDTY9qurGiVE3b5CRQ9OrDrPLf/rlfCAgXpH1MxdSxmCsikJtv2jhSCrnTJbIKrt+GGDnYBqqthQf7X/trzamBBcDAJ5IAfwrlasoSPyKx1v6aO8uA0X/bitknXRPUTNP4xtQ34jFUJ7BDS4/i5hDLfygz9eAURyfqTMDKZSZr32g1PBfHWuv4sx1tGKuNqRpqMStNFbfSFZ2v/Y5HyVmyNTBlCh9STNGbDohxw/YF/6Hn5IGR4aPFIBoIHuuJI+WNVAhT75dWi6ojQYv7cGRB/ssPxkfjJ8JrTD3OGD08+ctXCU6524NFPVNUG7BWKUUnlOsP4mLawpIrfnF1ai3yp6Sybin4ynMF4jxd0+n+wUHCaA0p+rJhdjv0yfRCV5ry3fqBg2rkD2wGyh4Ge911UxarLTKnD5EagUYGNZ1X61vJAzBU0JbbxdEwtg1lr4qM+4L7KiWm76FzoEclrk7ZnTVEujJLXQDTLqnUIKNDm4Y4LOC9IEYXBBJ2q60ewCIdtDUHxJMAj053Ldrcf0DugFyk1C9ysTD9+JF1aLGmrU2hydlEzW/AT5nHs2zUU43+OcvVUtA2e0wACR0RpuL6z68xSCEWfjYJdF+YIw8R4Z+KkWat+HXj8yynza0wmJBIccFqhI9/uWzOzX+OQczMk8WU+UyutdE8/ISXYC1x2ZYcE0OXGy7eKMl5CYWFV5puI0P6uELgG6mbyzF2u8P6LCuEnsPCjKaHBtJQrnr1zYEQkZig8Ot6aiO75dea+tqCOj/VhVosW1V5irTznvrpZUEwD8/dXEd9m9xOursgOQuNKgHsaCi7uwlSXoPbJ97nyZBQPQuc8AZhFhjuv/CURh5XXe7IMyn+wgxxIOgUiQzeYJ+xISX0fbd+e3KJ659BC8V+gdy11b1F8RzfE38g2T/Sn7gRmu1oUAzAfO228aWEwIT3OrB7KMHjJP6xhs6AslTq6NUwc11mAE3DOFbgnry1xmg94qpDWnNnjQab7nECFiRnRpykpbAgLzC20dsHrtt2/Hgb0qOqRVT/y1sSzI5WchaXGGD1o5MKI6aEFdcG4/tq9AW0Y29ffb0WlylviPY30063R1x8bLF6A/Ac9Ve0mXTmIWAs4ZRNd1mRlBCQe9MgncTI8Ly7uvx3BJeVZlyzFHpXQw4PX2SzzJkX7lSJ2s9osvTDAF+jxFgEPRD0wmYWSZVlwl7MM5X2xkLF0X/xelH5y+3VrWKs7H23XsGBjwjd7PABO5CZTa7MuzVKOZA+wz+fZOzi0y0KufK5cr8vVcbKBULtZwn3kvDUFM8GycQp8/R7nAyYQ+S1BUyr5lIKC5ySCuMr/fay+VKm9OdG3Sz/jrmIf8f765YZTEhrd/G5Q3JnmSXV0t0RgLmb5iF1aw/Kvqb784i77+P15jn/2pj98wW4C3C3jLh3ZF6Pph9k8YAkSfBxOjPAL+lklvNH0DTuxPH3vc5VxTSvcLU8IAYNqDyRZE29np8kT2pm+yg5/aeo2t0g/eDBz2mJtRW8a7P4U2Pb5+eIDeeCuhh0MM1J0r6yXiGW+usUcKpEqWPiWhG1Wv5YIPeD4heoCj4W9OPb0IOupZJt0yzPrS2U/xtBzk/nmrT9iMila2yVWFro6+/4ZVjQFEnxjRwDZNcupJ9B6I87FPmq/v8MqqbNvH7SHyNPLQk6ijLuqvURqx9GcwD8yKYftXIXqN8jjM5H+ymsx1FzdUPpVdVx6NJSbLPoZcd3pfDelpsxqPtrrH6CCtI1td1aaqdoomCq9l1TSFHexIID+56oiyC+5tuR4DGPacydaUn5pWeM2KZcXhnkigQdnJbo8ayi4rw7cSOwQ5NAvOR4pH8Wdm9C5u8R8i1Dcg9ih1pvLhq70ooPqj2RycOlYBUdPNWyHmr62GkEqxeIvWS0MUCc2yp+TSq37Py2O+7vAbSHVJ964Hls0ZgpjKYVz3VIzqICOmwraPSetm5u5HG4Hudg0NWO93j7Sff8I9oU9Gh7FASYmNCCrN09oNy3YaQ4TFSA3JEeW5/XDvA3i54HdmtI4GxXA21QQsgWwPKvp3GP+EDO0f1yASUtix+TYGjLOD+Jj8kuVD1+qRaNqvSON/8qSRZZd4e4eiDwd4RcAIgrmqjqcnjJl6ikR2IuxhBHb36enbIoBB3ismPFf10tyPScZKd+xkqmT3IFR3Q8Nleft1LSS+61pTPVKN2MgkCe1cHvLjlGDgORUVsv/Dw0FYZ0QFN9CTs9HFn9bZ8YkBIPqf6DdkGTVvgZzelLQQpeETq7NhF2tlzn1tDmTLPDOBk/7k3xJSUARXBfmyxTk4P7HxT/dcy1Y2lT0mcwp2w2PHz7NoKuRWIYRKHqwXLtmNNxvx9b25CGqUQ9Ae+wQJx6xoQYFgIjfGkpHBsrEmYu/Xvekq0J62ojhIqp3waG7Q3Hq2+sX5eZ0goc8/6gZcbvjVgDXqzvE+THIAM4HiEHoXEtENl82/Bk/f2Dssnfmcv+mdZ4iXcUoqNk7ogp7EclJvFyEcqtG5Dsc5tcg8dAwdp2IT64LQw8zsSDf8aYnKGMl6AikFup9c4W8G4kJ+4UjU3fOpBuc4JlOc90VHTRJVhsHjFCg1XOSNNs+DWzdKfQtV/AelyK9kGXx93WodW2BHb2jX8rvOO7yQDVvcq11Z1PXkkLw4RiIB4WHZzGxRTBWxOE6SqYOYW14wcp5jjfXz32Cg1Fk1V6DVbsjAYAtHmlUtswMG2dco8x3/2ILGth9zrYI4I5YRtG/qIhG7n2LaKe1avbybdaY/23sHpTqTLYnH8LtS/91jlBgP4Z4u1LUd+VRnNh/3v1RuEJvr/SeySDm5I35f76qw64Svb/u6Zv9SMUz3IWvlu0H0P8SL3XC5KGq26OlFBdzuG1XUvdS/8G9d7rWp05f7ho7icYGpx9iVZIIWm9ryBr0oPUfJ46fK6JBGzPiYR6FJa9UQ3Rrv1Hjw9pHqAeyYaiv02dBHI3zwBszfWiM0RzD4i5fN05HkHHTY8hRQEfF80jQISLmzgMKA/EgQY4kmRZffUWUlsP0ZXD15ZNEQjtABkxBV5VzZpPYu7kK4D7meAXyhsRfJL+OAJ6aaDQiWHlYdyzuUePd7bIpL8wfXhwFfgXilSMCPC7bnHAfOVMEWAhivTbN32UFidkZ+2gwQduLGx0YcRO5JNXxE518POe4n4L5EtuwVnOE/LQpsA0Zw42ivNSjAt3/D3QIMRXc4UK7TDdseg8oX7igGcLkW12JMDudYTWCt4UDUtUJWqO2YnTxmZNpO96wzIIYx9cvDrjKkqU9TMb24fOeHWsk/kYamQZwPIRRtdFczgjdhUj3U/r2QAjUlB1DinTKTjqncgerYQbA6qTMNgD/vTe9plCSorm4l7Tj5s2/PqTXbks/0rlH38KweeMaadnlIxdOz6w4JYAbNmEU6vleYgzGFOI3wUJA9ebWQAYksNh3l4V2LY13qItW1jAQGK4yoRZOV/tus/bSWntJ+8+GNq+lxcYLpJlwsr60g8+3eqOiUU5K+jcgS/FZtcOA/Sx9mO+cMQjJqaBrf5HTNeewk5q7yJLU3fH5s/bnaCtBaeZoRld8QTaEw6FBYllelH0jfXPObwXmVIq2+d8f6gViZ2JIVctHOSanwjxo9a5OPCoqrP40T9gERIBqziTO4hZuBJI4UZ4uxn7Q6OGZTm5NmUqmc3SlJWW3NVZXa7rDl/x++lQ+oXvCxLN6Uj3hvmUxlBxG/6NMFChiSihS4jkAZ13BIxkCwmcgTKQNYu7qt7JeZPXUtjsYpkSAC9EWzlVI/kNSkNJVmLrBwjs6Rscrjt4MNUlUoWGpRj0RKzDdQRMjrKWQ6D7CNKjZVMEv2JbnySsMWYP72q0nSY0YUNKU/q2FS3vCYN00wKsnSx7y74fXnCoZe6vzLyyG2/QRjAv/1u1n/ppHU/AIhJz4/ZvRBtR03yHH4digOMcr25O32L5mGaAyfb6VDdr8eSRw2hJLMqIPG8l+d/Si8J9eHDGAXftfbWoWaJxJ9wDEDQgeAlRvewK5yzi/1lXu4tX/02oMd8CTgFjnoJnB0Yq87HE6NLLWk/cjAs33PQPMudfJ7A2sfcgeVVK/4dHcd3hGP2ROUdiF5LfaBqDxDbJj1ledaowW7jCJRyd1R5xBMPHKEmdM9he60nb5p+dstNqOgCM6UXsxZM6yYw97ZJrSLJRwpdmOKoJ3IrksmVzKTxutdUackfRdcxFvl8U1E5KSSGpszHGFv504ZwN6yNA9IfkF3N3zrtET/Gc1ahdsBW1rGzwaE5pPzXUrXu6w4nvnCADScYDMGieujkpAmkTVhYZP4nGgLJrZ8e8iN30455LpgTYVLLffIgJHqhfyooYa1oC6fz3aB7/tgkcH7E3iyR0OenKw8yvHWugAP2evwbb9bpoa3N1XvPopcUyzhdghUvZRLtEdTpnPL6zYChl61oHDSVqJOpre6gcCdKMqEBcE/qI9N0nrYc91+kL2Y0VVfvZc/lqNOsh41SgPsJ8L0pfYG6nunShs/1b8OBnYdVwX6yE/4W+QGIuT+2mTuqnHGpsvV7V+uw9SDbSV/3W1BpVpjk3NzVSa5ggwi+Bl2ApVrvLl0iGM1ECVjkIX/RjDkFKQwOdzoFK3OUACLMTfzGBwNQB2ozoBTD+dK/SEGg1a3d1zgl/BdK50iFrBKvbKd9hmeVkbcKGPOfyG0hP9t9KhB8904w7kFLtqT3XiVwJwz10le6dTcnlxX6My+gkl1Vv/3olKORfxBb0oB5C0gOCvPohPMEvMSNknvTNKBRdmKCpgQ8oM0iOkFeOxKtfdlZVXPJW5JU5D4UaDmPSMzmqYFhZ/SJFrg5GpIbNRHFI/YozaT54qzFSzwD/A6+2bIPY2v3HAasz/RUqJ4CWouhFtqY9RBMkosQX7NFVG+rYzqdWhxh6/g1PgN2njn0UbmHDmBQiqidakQT/fKKIkuYhqw9f48lrKfbqqixmBOu5uFK8ZJE8tqGqmIsc4UI22h1p+pAisP0Bep5dka9HE9bX0X7A1WVer5dBb+mFz1bLXH1rR+2W7t0/r0KeBQ1XRZEO/c7jvsRP63NJBbRg8IKQI5AxdUVlslnMbzCfIGaooWRaNyh16Wsc4QRPWzXfiKbl4Y+lkWLsz1TbI+flLy6lB3iFZA4NMTTzkhY1DamInbcqz08OkyEeLASwc3UyafTudnBGmnBXutqylDYAapDO0myioDkjjHYeQ5sJx37HTfy2RT7ddwSer8vmMzk59s2o4Rtb68bAz93iuzh66SBF/9kCs1rjP9gxHxGW5cfXSaZCTMCERdHtiOTKb2EC0fPROEMjp0qvU0yPb2MOSzGW6FF4wAvZk74vrvukBb+v+Megidk0LSIndzocANPf+ADrHPB0Z4K6/9rCMc3tyNBEKuHI7Y5CYbH91WOY8/tJWTYdm4p3I8ISliDKOSvUN6YmPR0DEegwcggD1OCMSYdMwHzeY0cjglwYKyZdBlb7XDILilyB1HyDQ5JiZRs2Dh5FKBMzRQlQVBM/MFwgkbaS/82hgEfJyhEGFRmGgY0+9FdJYlC4ITM7GUAdkYxRR62Sw1aGYUDDWPvTFgUERbMygDUXcG3hAH+ro+hwJKE0Mre7cZ0FWqEztWjrtQFdioiLxdQDdBSzf+6utsFCsbxLKll9LR60z7cxrkA2CRPVzzmLZU3APCLO0mmucUXd1mwc2EDE1JvAHs6n7x5HTK0gbKyJs4jCRBj9wP7weeiii2G3zfR1XWX/RF2juekt3In6SOkUoX3sZCnMQyjLteUfqsHSq0mT1HSA35EmGQYv7Dxu7BhgSFN6JGAtncjd1c63zmfps9ZCEeSUWVLrSSzlyjrSPtop5B0ZnB1tXJr+lJNbE8tWM6pvnjwkO06710goU2T2a9JDOxdVd/mmEiRf1S6tF9bRPwS5kKBCwP9M6jfy3xUpNmzIE6bFZ4qB6a1ZADYw5YNz9s9r0OndA+f5BZUlMmWjNqqmduJO7zgZ+Ssd3YmoIdnoMHlClqA4827xPlCWMBNGV0t/V/7dBvQX4u1Gnva0G8AzWRWkHv1ZtNTT23kd5+38F6ejfYkZLYK+ZDVKs1bgI+saYxD+FmkEyecwrho7cdzwUfv/K9K9H+KrQQ8251bs2fBj70HbNXMRzpfAgCVGTEMYgaz4vMFCFtrXuf/wW1KneIIi2X5/4Xwkm0L3q5bRrjbsmPcCjLvvaWEo9jBu9X56CFNM8INcWcFMs3zqr02cnqRdWivibb8eSzOzxNlJbUy6XrAG2TC92/n1Po7lYaAEKNovoym/hPtoXUUsFPJ9SsQs8L7T2dxIAt3cUnIp3UH5K6yJrpie9CtNNPWaG0j5A8ZUmJdLJll0wzWl/qcIl2dGeeY4ymLHt47QNwWRX1V6i2T9NERPerHX98NqC2EFhbuTAbOZXMlVd2IoYvtSDQivL+9e60TFqhyVDAFIC2d2rjrVY5dAQPpdBOfgejtu7AhgS9iqKvXZpgzKdrSgEaxI96msCIS1u1u2mgF3zuT9toAAh173x5sL3isJ1N/tTfg26yoDB7jx8FGLcVa2VTn6qAElbydgfAQtQtV+hZJOPgWBSAXUWRiUzHM4qGNXg6DmbTsKXdBbXOdo/6NCV0K2LXxarCCMH0WuzpIRElB0ctcU1mGGoF3N0Ef90kTqySc756iw1gcUk8W3nljADS6GrDRGzmtb1qAR5oJlxv3anJZ2pqfARIng7+UuxXKIqtPBfA/vUnAwUwZLoO3cLOSK2wrj+Zp0wAYnqwb2qAX3lyKKXsAZNheoF371IyDHbHz5VL8yPMvBwhODW5gUw0u/7VEPBu6QJlUSKsfXXuvO6IIcZ/xNXhtWRA1CM2RZE2edOXjOnp7ivDcBzRRh/QI0nknVlfBsLZEROH7IUkupYlp33t1EITNQT2Zpz36BQu1/CiCVC1olxi5ts3vliWYN+7OZFR/+xqCZ4xe10woZpBtHa6B9IXhjHT9pEWU84RYXfgSa6iHu6P16z8mwo7Ont2YxJ1CGmzgnu05lGHV+NMR9YU0uTEO8EUZu0B3GbOBVQay8hj+o2RTEOvhxEE9zqKYFrTLZIqdWwne3nEXYYuR4FGBWdDG04CgZ5vPzPhMXFQmOPDohkF1ny9KXBD61VpISWniI5ooO6MsRGBpJUmQbP9fl2pI5V0Mr1oEP6YavMdOLTXtWtj+EJOUkYrijTtW+Dgx+QPKp5Y7Z7lAbIrHe/SZyfe+VwN7X1VnvKCbf+mYskH9WtikPACr3W3kAMVZbEhxQYbCIcFi1G3ryQBdFbFZTD0PLqSosqR2zz9J+LvKJWnGq6nSPZ+yk795eJAz3WD2hdELDplgd3p9qcQAQ3oXWZR+GyS9FP3zpM/l83kcEDvXK3E0NgcfD5dbViTsFQxToUhr1psHQjGdFd2CeUqWFubwRzpOS3jg8MKnu1l2INGIzSvv4x9dodjQR1v9SikTiutBMm10a82eeDBU8ML0xwPxgePCwuYGpkGiHv92wcer2ER2l82EAdVhRLftalMXRpvoHD8wPYcwt3Swk8U+CgGAPJISodqcQUlvCMMeE5GVSFGs21rh7Q/k8AeqQuKGvDdOixfT9CmhWh/gv4f9dsT3jnX0DjKeRL6Mo10ndmzjgjA85kl5zY2Al8mn8TC2vQsJFlIPpMiooaDT3MLLCXMMKZIBkn8qX0mZw2MgEj0noM45c/4w4pkwpfzyLc2Iu8T2CsHD9nSK0+3Pk6ei4BoJEbh2c7TdjQqcCF3TOiE5sUDeEOmOK4ArQx2QOQ1mhVP+XXHQ4/4DgHz8QFMK0kpsba2o5Zob9AobYJhnwjGRXM+vpR0+aA1vvzzqUvdVoM+uiR5H8/nTV7Kh1voPmsRlT8SolOIQZ/p3zLO8PXnrmhwMHJP4TM5Ad2S8BlYq50/4fdijRBRby11w3k8r7LbM8TguSMYpuTsqs8448YVT5uFZ0EyFdBO/NX5R1FzEBS13uuyaKqkMHbuyu+umE5Yo5OWIemUQ7MjI+oZBEai1HGNHorAobDgJyDK0ByCR7M0DHaDKdFGYT3qf0P+6rtP0lv2NEefX9S63dOTNDvBJXzR9hvUGs0GpohCzec+W+YQ85gYs+VMf7sYcssyO1ss8WO27jFTK+etJmGa6UmGxTpjpka7YWTo0CvF3JkKqLutjchxMD+XuShjq9IsHAgaguyBRnVUQ1D2fMDJrP5kQKY5Jm/opFS0E1KbAaLv0dm0GwsxVrLblZM/YL8W/I/AIrIdJ/yEwr1rfO1WZNwaNG9X9/40bOfJG/euUA8jY9kuODxsvq3TgLoGyimRaqMpzbYoP/wCjU1FJ4wkJoGgLfhloYXJ1mtnclMrP1kxxMAVtj5NT190HLlt7IHWlQ1jiDr2Poj7XuzVgzt9BKs665MewJAmEpJbKsJcUPZ/XOPGCNc8LiaUnvZg9yAf9jLRUXZ7nmQjb+sSYY4RiygCKL2mMY7mBnJzlQG+PcW21GmkUrW4RQpm1c7I45cot/vXwnsHKL0isPfKoDb1dOEMi6XfU6GkQHi/Qsyz2179f22rH0gAS3t8ePJIEXkxXzCmJotdlpbZ9j5WnxkUXTq78cKL/JBsQkQ77CAbe+X9RiMIb9a2ieleA00Hgkj0Ep6NCiSO54hPafQeZMZIOhqgjhFKGGfsWURREudzXQJ2gx0H7o7tmPSaonXogTnu2sUTrH9/W4xIMvmP3o9BOoKGDJWFjXO5u6gyWmqqNf+SoEFGr9f1G7qYP/X0aSBL6PqoS3UirOeWvOxL+3qMAvAPmG9hUJ95KLNo9PQs4R9usOSSPyYGJ1+fS3WuBXXy7oD9wlf80GYXL/wiVuU+6VizxMvIiqRLC/K+GW+Tm5++KjhzhIs87EjsUUCYHvoBwmR/Ss0fVWq2fD4xu0mgWqqJHofiY4LiJcXWwK13yVgaXetCA+pUZ4JMP3HolrHSuaL4CtjoJoSaYeM8iAp3+CzXjBtR/V1GuJYHgLONEehXAo0L0Z7ipFv0cQbGB5ss8HbLlaampla0EOd0PVirgF5KFO19SOb3LQPj0LVJPVspkWGYvy0V3Hn6Uo1EF8qdH6yktcNhXLF7ehv0Td+Pjy6uv9A79XK/OoEI5lvDXWX3JWgfEBeXufeZc30Lh+Mz794HJD5Z8nlaMRWPSofNMzOTN1q7pBk/+0iHyi5dwO/t1dtVRlW/FpAkfOIiRmVRefrqraRaD8bqBeFNRmA3G9WwLwJir5pkIrcSnQcKbqqnx3gC+Z3rjJCW4brbBeyiqREhohWznSSNWWAcTH8nQEJB/6+XU0f9mHG/Wir4tDXDMOSf2hcWBzz8F286Jy+VFuaqSQ8jpGk61Tv3YpwjyhLIv07YIwol+LYnIt0cQHH0ySeU/ymi3tZGI3394S2+LioZtoAqf+ZvOfFogrfUA25e+Dmxz6PPEtIvpNpmP5Rbh6KGooommdujSBeve6Z+Bk+0l3t9TxXP0ZhcsE7ov7XzktJ1VZnc+HGQeDLp/XtuOV6VFL/0ZVmz+bnOm3MzcEXRtVy3ywg+TOsOdzJNwXIP34t2Rz8mwNQgvfH8L6l577TEbiaqYVMcr5jCvkgDiF/JUxXqFRFyfWlxVOKd8AR1PzZXLh9GSYbv85uxAjSSIuBp5H91dbBdE8c+BHchfZhJDSIxBX/Izx9P5vmxABTH85F6Trx9MlKUlT9JYczm0kZGY7l3eI99njj4ePOnkfuKumHU64rSe38nQ6awbKOUPIRyBo9H7eOgZ7h3lqbHEdMaO0OmDovXgw+4y00/NyNAM8j4nScWztlGP4XySzuV7d8BmiUg8eC5syRR+xA9O3hILausetAebfAgg8b1Z5r4Rl4Rmqv/BEje486MkXFoCMzj3Atkl4Bnvnl+fE0Y3ggk2HTd3a/J4VC9LWH1Kyi5NcH4suJeojPa5sCvmZNjNeWudaUxG+Lnnc2N45msq4lVfkk2lWpurctyNLyaCLUrYiG80HmxBZaU1Ukq68ideUgdzqmQsR9uRfIWPSr2+zCQIgZppr8l5u0QyCLte0CwyxKvfAt8jLBmeEr7brNE8YDOTX0hCaGeyogOWEIbstMTcAIsPEbmTGCOT/GOphHTIy6ZMn5gESI8L8Zhz3eh5GNaQlNoMv10QLVK1zMmE9ujYoriuNA8YvVsKDynHxvZqQ+lJPAJsIVhTTrpCqKURzjlAVRYhNlrkeWo5WOX6ER12yXq5EslGSRHWIeOGfb2ZC9seY3r7RqENN3dECpDAMHF5qrSGzRpr6S8fjei2gRkp00sDbt+euGC6Lrh17LHd/O2owW/ffL03Lgo2Ys67iQOUHKh4eM6qp/A602COeFGluqN6u178MR0pS62bwtMfXSyAuD/LDx8kfcDqnUwGWrYidTH87DxnwNCgtVmvNpGNmw8wgLFEZP69iXa6wyAnuixC82VM3olJVPHyZrLzZSP3yuDHOcCk2z1t+0AvOzROA7uSf1Xfug8KTxEB04yMu6qJIC4cirPZ5nDjU0W/s3FPaA7Wp/EvCEV5k8Vs/s5GV3namw42LkpCfEU28osCuonZKGspdvOoKzQpFuIRN4NcWIiLvemOTyHfIFabTSJga/G/JTMaeRP6z/SOR8J0MpCM64NAVm+K2bTk65fJLh5aaX4dguKe1NKf3Ih3CHdzSMUJRq85rbC6tUYPfDM1PB+dz6vzdLIk8cfhuiPg2xFlRtAkeHZVl0vXdgrv40knPioQXz7iS6lDgii3X4Zm2sPNe+Y2825UGyK4BKKHm3YmuCEEoM0qcf2cJGkLcAoQD/uTqb9nbJNvLvg79ITMvH/GJBj6hxblA80u/c3Nk77rRsMpsZRaJ2LCmVcnMaLd/L9IgHuMDYjqNDzyxsHtvykilY7cwFz072U8PEWenM9+Zd5UPAjamZ3L1cmO6TzifG27/eGNhRaut+P5o+K6mrSmpxp6L6KlgU25/XQ5GiEqj3dzm2Ew+jIvKolJHSuLxoyTIA5EFbdpRp/PIyaqwyZzcmSzKBrrRUPbXKTS0h4rnKMc+q6AlQ/UL/RScYuyNJ8REkQyoRaJUcgKTF9A5GK/XSGWiF33axg2eFKC67sKKG3plsxX7P3gEAkXch9Gz1C1QiD40E0EhP/FPzeVYKbkFRxryuxccvVqdkH2OCqJBL9GLm0rkoaKOUmV9szZmuEP8zW57+gg0d4CUw8vRzj2a5tNzsembRSPALsKHgk2VxoQe6LOygctY6/10FF7V5Twiabfh9p4wnm5QJv11h3V3VpEPcR/4pNhRD31x3pUgQtKipCKisNUxj0m5MhMUvDyQaqDjpvUapIqhKnCC6uuONSneQa4KfN18cqRJkMT4uhDCK5lkXMpsekGArcEKaPfnT9qGiXKD6bn4M+pAIkXbdcXxFzcpGbBdvpIrY8+HlYBXjA9+3zxzmvgFxylRkFaChK+nLHPNwkRVSmWPNI/G3cXeualq+/EW4vLjiuD+JUycKw9HsNfDMQKP0c+72Ccy+EXdDsJXXwPeBmMWR5uvOcTQ1JVoIGrr7J37eJgaqJeq6htVqJawgQSzKQl0K7KrbfFdvJcPZwctunCcfKxIlKM/CeLf92StPBQ3n+0yi84u6qE7R885Fuh4FwhDoS5Rx2I69SY6cY5HDueEuWRkca1Ag5JQTYoFme2Xex3yr4jWhxLU2FvlxqaVMEiJy5iyPHj5ykrkNW8ueYjg7AWAEXbCjwN+7KDPQBwJLPYrbMBBwE1YTN1TiBlOcfLSbP/8dZtSvdqn0SlhY/bej5OTnEuZHCXdJkoWIbZkH/pkbp1RoCjY+3p37D6hihrRlq/Gsd0WBaL1Ei8jmgbQ4WJH8jCDh36XOUcPkXQdVWWIepRKF1DGy+BbQN36SHQQ+ARevnOvr3TwzoTqr8GUguyXoOxMmnRM4ZlFNAUNVzp9FW0lU51W2IkoGKrA5NaMbsUtL+2PnaJ30AnoWQq6KS4nPT6N73LNqZIYYOY9SG6+ZWdb1ymR+6sdu+Du6bVv8JOJMKZPyamMDYNJZMvZ7+ELxuFBrnWxnyAEJUt4+QknwAxBPIAFXxU/iLh6l+UzAJ1INfVdnw6WXwJAF7gffbCr6pfK7OJVt+jF7CciCRapPuJYMusD8Igx7styFCW/3sBgs/xHNtInv6vjgXwAdfn769/RMJAGGq+uy1HjUOEw4u8ycPuPbzBpmWDSYy++FMsQ62cbBTsaRAPaYpwQbUbWDY2LSJa0gyVz24AvzRWnfvd+fNF53LNemL7SxYz98nUuotD00sQYYZ8GL9f5YXiBg6ciwGaUPTqUg7YiH9HuSuWePigZ13d7GTzInLZImRWJE/JSnMn5VOhgoNURMpNW9FEbFZEqs989lXA3FR2KIB5pCB97b/1mw2r3tMOcSnCr7miFQDN/snUbF1pG50Cl2aLrXQXdcfQKtGh+eBA1AjYv8UlnbKPXCI1B4L9oJIAlhKRXmmxOj0SlKvYZ1QmEV5VTqpeUmfhnxK//AO3GiT5rb7+HaJQ1+dZqS66lSJfHJSnsqq+rWjJoPZwhHIG67Dro2enPlqZkFpX8Hpl/IIOjUQD8t1CeMt5BVMDW/mcFXvollPz60s+WPpfOEu4BVB0Ck+3+rfcizxrbUt3k/aN1eHpjRfN0jrXQqYGPIZfDmfYuGOHL074YJ++Pr7HheAJqzUUZdaxsoHLPNIfrUcKL9mvG8xyM+nvQq/ZOI3DX/WcvVwBUgBAHRFfVyYJh3FnDWdtMvurfiZva8RA1zxFOtvMZiPp5yHmpspSaFiWpLBF3KbgLeoMnGynZUwdMadrgJdHmz2aH1lMgL6sNzPC0xsQnUzjnp0FUcujI/nAi9ZHPdn4pAsacvsC9yinQN8lK9gWEEo94V5fwF/rMMrD3yrtoV6MxDcgrsJa6RSVmKm5nOhlULrnrlyYzS5XERRhOHO0POn7KYtWGLAigs8R5qp6p8/K9SqieK7alBCkSxD0ibF9h1yY6yEj3VHIdfBSBiyPzJm94clRgAN6IdzoeYeqwe/J4kssslBO+wUx1jH7mUjVQl0sMG2WiQOQnIF+pRT2N35hBkMb2Tu/FDG4wFaH/Zas2MXhS2Qinn4WAb1U+t9zUMMN6ma+LrwWO4VCpqowgKdKluVvBSJoTf1/si09oqHxYpixuZcwJ/AEUM+Se5OqymcjzuyhONBa8UOQnwfcaFXiaITdlhBYSQvfZI//zwBfWD+VC7sGlCvfOb7BgfYgjextpthBwVrKxf696SCFAxNu8HwS5tmdG5V08LyUrLO5nBn0VsA5QjVz4MEE=\",\"id\":\"XOC3ML741wpEIGob+KDsoYOSbczZGKnUlXYFZSSIq4cZsQuyfdyFgH56azU1QmArKNPY710m2WSzGpmq7H26SXVm4HQxMC2ma2HTKbzagLCDONsDKETnJUoLuukF6RYQeprFYdY1FIGDYJ3jlJ8QZgA4FjOV41cOoqHkXU1eF731aGbdOGQh78onyWgsLewvUGuGGlpORFn3jSjeYCLqrgvcbLAuhl+ViewEqb6s+GBKt/4K49GlU11vsXXQ/7E+KXJA0H5t5Vk7MPniTB2nNB6JYqD61Y2p5UNjgN2wBp3b06Om5y/mk+zwKhJfSZaUsHeL7LqEWnwnWBiAgivqBYi1iUsRF5eIkQfhYV3GZpbvMTTytQ189RkTLnyile9JEOwYyRwOv8x1plvytyduOmlGiZcl6t4=\",\"summary\":[{\"text\":\"**Considering skills for task**\\n\\nI'm thinking about my task, which involves extracting reusable pieces and normalizing tokens. This suggests I need to focus on three main skills: extracting components, normalizing tokens, and frontend design. The instruction asks to choose which existing frontend skills to load from a specific directory, and I’m considering whether to include frontend design. However, I must also remember that the caveman skill should always be included based on prior instructions. So, I’ll need to select extract, normalize, frontend design, and definitely include caveman.\",\"type\":\"summary_text\"},{\"text\":\"**Selecting frontend skills**\\n\\nThe user wants me to select frontend skills, so I’ll format my output with exactly three lines: Skills: ..., Order: ..., Why: ... \\n\\nI should include the caveman skill since I must always load it, according to previous instructions. For the skills to list, I'll likely include options such as extract, normalize, and possibly frontend design while clearly noting that caveman is crucial, even if it's not part of the specific directory mentioned by the user.\",\"type\":\"summary_text\"},{\"text\":\"**Choosing frontend skills**\\n\\nThe user wants me to select skills relevant to their task, likely focusing just on the frontend directory. While I must always load the caveman skill, I might not need to mention it explicitly in this response.\\n\\nI'll format my output in three lines: \\\"Skills: ...\\\", \\\"Order: ...\\\", and \\\"Why: ...\\\". For the skills, I'll pick extract and normalize as primary, and maybe add frontend-design since it's mandatory for normalization. This keeps my response concise and aligned with their request!\",\"type\":\"summary_text\"},{\"text\":\"**Selecting skills with steps**\\n\\nI'm thinking about the steps needed for this task. The teach-impeccable skill may be necessary if there's no design context, but I need to check for the .impeccable.md file first. \\n\\nThe user requested skill selection, not execution, but choosing the right skills is crucial. Since normalization requires frontend-design, I have to include that skill as well. The likely order of skills would be \\\"frontend-design -> extract -> normalize.\\\" This keeps everything organized and aligned with the instructions!\",\"type\":\"summary_text\"},{\"text\":\"**Choosing skills and order**\\n\\nI need to come up with three concise lines for the skills being selected. The focus will be on frontend skills only, specifically \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order is likely \\\"frontend-design -> extract -> normalize\\\" because frontend-design gathers context and can trigger teach-impeccable if needed, then extract identifies reusable components, and normalize realigns the UI to the design tokens. I'll keep it clear and accurate with the instruction. \\n\\nHere’s how that looks: \\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: frontend-design gathers context and may trigger teach-impeccable; extract identifies and consolidates components; normalize realigns usages to the design system.\",\"type\":\"summary_text\"},{\"text\":\"**Drafting final output**\\n\\nI'm preparing to create my final response with three precise lines. First, I need to list the skills: \\\"frontend-design,\\\" \\\"extract,\\\" and \\\"normalize.\\\" \\n\\nThe order will be \\\"frontend-design -> extract -> normalize\\\" since that reflects the necessary steps. For the reasoning, I'll say: \\\"Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\\\" \\n\\nThis keeps everything clear and stays within the three-line format. Here’s my final version:\\n\\nSkills: frontend-design, extract, normalize  \\nOrder: frontend-design -> extract -> normalize  \\nWhy: Gather design context (run /teach-impeccable if needed), extract reusable card components and canonical tokens, then normalize code to replace hard-coded values and migrate uses back to the design system.\",\"type\":\"summary_text\"}],\"type\":\"reasoning\"}"},{"type":"text","text":"Skills: frontend-design, extract, normalize\nOrder: frontend-design -> extract -> normalize\nWhy: frontend-design first to gather context (and run /teach-impeccable if missing); extract to consolidate the repeated card pattern into shared components and canonical tokens; normalize to replace hard-coded values, migrate usages, and realign the UI with the design system.","textSignature":"{\"v\":1,\"id\":\"9XoYFdQ5cuW5usbAoRC3/ZNYbI24ptf8dmu0cbRRyNRhzcasNoevA/i4gLP3SP/7541RD0qFn+tznFTjJo+Lcydr/pneI1z0aQpiycOtz2bU70N7+oQHOCkh/UYUwRRFKkCoL7J83pzhNACy83F6nHPl4rb9DmYuMk1bGOOCguhC0LS/vTmYQ7yNchCq2jBp8tvq3WJhVD1Aa4xKriH5Kh4sZ9XNz/ICzzCO4ET0GhNt7IswKPkGAaD+WwsQsJ3a6YhtjYXd14W3bhvy+1/x0LTAnjJ9S43g5pHqtdskU2NBRktZ+Q+BQgBO3Qzvk45HK+gHaNChIkWhJ9l45aeVPZMThj9jw+BZKmQeV8RE/wZY4G52WuMbsaQcvNho087kp078ldf/TmhS8LYvXoegWsMhbWxw/WPr\"}"}],"api":"openai-responses","provider":"github-copilot","model":"gpt-5-mini","usage":{"input":7101,"output":2806,"cacheRead":3712,"cacheWrite":0,"totalTokens":13619,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"stop","timestamp":1775941498364,"responseId":"RNStl7VdqppAA8Mx9QF1JPr9udtc15F0mYZem+riZ9KHF1VI9IjGmHPb/Y/SLTVvp3q8J0eQRuRYoA5xX343cHZ44rzO7igFycJrxJklgAUVzYsK5PQIbe9P7JM18EOuZuw14ayNNdJ/HOb9/YQF+3nYVWtGCahicg2gCCjiWKjnOeC7CANXVNO1qzymrhdw7kg6Im8OQ07ufyrhUvsn8iOJgv9IrAd/z6fiU/xkcIOxlQBKusUdYo5uM4aKmrGIjnJnpgBUvqv03M+fGlRqd/79kk2Oo3XFy9GyznVsOoq1IOsefZJEQ9gI2AF9KwcdCzGiwJuJjJhJFSCHsdtJYNeO5oRNXnBNP7v+UmA+/GVaC9dvfsb6hlkdY4qycQKfu69CGeo9rixZAUQnvjABFdS7AhBo2prVIw=="}]}